CN113641718B - Model generation method, search recommendation method, device, equipment and medium - Google Patents
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Abstract
The disclosure provides a model generation method, a search recommendation device, equipment and a medium, relates to the technical field of artificial intelligence, and particularly relates to intelligent recommendation and deep learning technologies. The implementation scheme is as follows: initializing a search tag model, wherein the search tag model comprises one or more tree structure models, each tree structure model of the search tag model corresponds to a tag type, and each node of the tree structure model is a search tag comprising at least one term; editing entries corresponding to one or more nodes of the search tag model based on the user search history; and modifying the structure of the tree structure model of the search tag model based on the user search history.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence, and more particularly, to intelligent recommendation and deep learning techniques, and in particular, to a model generation method, a search recommendation method, an apparatus, an electronic device, a computer readable storage medium, and a computer program product.
Background
Artificial intelligence is the discipline of studying the process of making a computer mimic certain mental processes and intelligent behaviors (e.g., learning, reasoning, thinking, planning, etc.) of a person, both hardware-level and software-level techniques. The artificial intelligence hardware technology generally comprises technologies such as a sensor, a special artificial intelligence chip, cloud computing, distributed storage, big data processing and the like, and the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a machine learning/deep learning technology, a big data processing technology, a knowledge graph technology and the like.
In the prior art, since a user search request may not accurately describe the actual needs of a user, search recommendation information (e.g., keywords generated based on the user search request) needs to be generated according to the user search request to guide the user to further clarify the needs thereof. Accordingly, there is a need to provide accurate and comprehensive search recommendation information to users to meet the current search needs of the users and further to motivate the users' related search needs.
The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, the problems mentioned in this section should not be considered as having been recognized in any prior art unless otherwise indicated.
Disclosure of Invention
The present disclosure provides a model generation method, a search recommendation method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product.
According to an aspect of the present disclosure, there is provided a model generation method including: initializing a search tag model, wherein the search tag model comprises one or more tree structure models, each tree structure model of the search tag model corresponds to a tag type, and each node of the search tree structure model is a search tag comprising at least one term; editing entries corresponding to one or more nodes of the search tag model based on the user search history; and modifying the structure of the tree structure model of the search tag model based on the search user search history.
According to another aspect of the present disclosure, there is provided a search recommendation method including: responding to a received user search request, selecting a node matched with the user search request in a search tag model as a hit node, wherein the search tag model is generated according to the model generation method disclosed by the disclosure; and generating search recommendation information based on the hit node.
According to another aspect of the present disclosure, there is provided a model generating apparatus including: a model initialization module configured to: initializing a search tag model, wherein the search tag model comprises one or more tree structure models, each tree structure model of the search tag model corresponds to a tag type, and each node of the tree structure model is a search tag comprising at least one term; the entry editing module is configured to: editing entries corresponding to one or more nodes of the search tag model based on the user search history; and a structural modification module configured to: the structure of the tree structure model of the search tag model is modified based on the user search history.
According to another aspect of the present disclosure, there is provided a search recommendation apparatus including: a node hit module configured to: responding to a received user search request, selecting a node matched with the user search request in a search tag model as a hit node, wherein the search tag model is generated by a model generating device according to the disclosure; and a search recommendation information generation module configured to: search recommendation information is generated based on the hit nodes.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a model generation method and/or a search recommendation method as described in the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform a model generation method and/or a search recommendation method as described in the present disclosure.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements a model generation method and/or a search recommendation method as described in the present disclosure.
According to one or more embodiments of the present disclosure, accurate and comprehensive search recommendation information can be provided to a user to meet the current search needs of the user and further motivate the relevant search needs of the user.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The accompanying drawings illustrate exemplary embodiments and, together with the description, serve to explain exemplary implementations of the embodiments. The illustrated embodiments are for exemplary purposes only and do not limit the scope of the claims. Throughout the drawings, identical reference numerals designate similar, but not necessarily identical, elements.
FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, in accordance with an embodiment of the present disclosure;
2A-2C illustrate schematic diagrams of a user interface displayed at a client during a user search, according to an embodiment of the disclosure;
FIG. 3 illustrates a flow chart of a model generation method according to an embodiment of the present disclosure;
FIG. 4 shows a schematic diagram of a search tag model according to an embodiment of the present disclosure;
FIG. 5 illustrates a flowchart of an example process of editing entries corresponding to one or more nodes of a search tag model based on a user search history in the method of FIG. 3, according to an embodiment of the present disclosure;
FIG. 6 illustrates a flow chart of a search recommendation method according to an embodiment of the present disclosure;
FIG. 7 illustrates a flowchart of an example process of generating search recommendation information based on hit nodes in the method of FIG. 6, according to an embodiment of the present disclosure;
FIG. 8 shows a block diagram of a model generation apparatus according to an embodiment of the present disclosure;
FIG. 9 shows a block diagram of a search recommendation apparatus according to an embodiment of the present disclosure;
Fig. 10 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the present disclosure, the use of the terms "first," "second," and the like to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of the elements, unless otherwise indicated, and such terms are merely used to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, they may also refer to different instances based on the description of the context.
The terminology used in the description of the various illustrated examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, the elements may be one or more if the number of the elements is not specifically limited. Furthermore, the term "and/or" as used in this disclosure encompasses any and all possible combinations of the listed items.
Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented, in accordance with an embodiment of the present disclosure. Referring to fig. 1, the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. Client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.
In embodiments of the present disclosure, the server 120 may run one or more services or software applications that enable execution of the model generation method and/or the search recommendation method as described in the present disclosure.
In some embodiments, server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In some embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of client devices 101, 102, 103, 104, 105, and/or 106 under a software as a service (SaaS) model.
In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof that are executable by one or more processors. A user operating client devices 101, 102, 103, 104, 105, and/or 106 may in turn utilize one or more client applications to interact with server 120 to utilize the services provided by these components. It should be appreciated that a variety of different system configurations are possible, which may differ from system 100. Accordingly, FIG. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.
The user may input a user search request using the client devices 101, 102, 103, 104, 105, and/or 106 and obtain search recommendation information and search results from the server 120 through the client devices 101, 102, 103, 104, 105, and/or 106. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 1 depicts only six client devices, those skilled in the art will appreciate that the present disclosure may support any number of client devices.
Client devices 101, 102, 103, 104, 105, and/or 106 may include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and the like. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, linux, or Linux-like operating systems (e.g., GOOGLE Chrome OS); or include various mobile operating systems such as MICROSOFT Windows Mobile OS, iOS, windows Phone, android. Portable handheld devices may include cellular telephones, smart phones, tablet computers, personal Digital Assistants (PDAs), and the like. Wearable devices may include head mounted displays and other devices. The gaming system may include various handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), short Message Service (SMS) applications, and may use a variety of communication protocols.
Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a number of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. For example only, the one or more networks 110 may be a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (e.g., bluetooth, WIFI), and/or any combination of these and/or other networks.
The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture that involves virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain virtual storage devices of the server). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.
The computing units in server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. Server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.
In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client devices 101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.
In some implementations, the server 120 may be a server of a distributed system or a server that incorporates a blockchain. The server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. The cloud server is a host product in a cloud computing service system, so as to solve the defects of large management difficulty and weak service expansibility in the traditional physical host and Virtual special server (VPS PRIVATE SERVER) service.
The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of databases 130 may be used to store information such as audio files and video files. The data store 130 may reside in a variety of locations. For example, the data store used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data store 130 may be of different types. In some embodiments, the data store used by server 120 may be a database, such as a relational database. One or more of these databases may store, update, and search the databases and data from the databases in response to the commands.
In some embodiments, one or more of databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key value stores, object stores, or conventional stores supported by the file system.
The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.
Fig. 2A-2C illustrate schematic diagrams of a user interface 200 displayed at a client during a user search, according to an embodiment of the present disclosure.
Fig. 2A shows a schematic diagram of a user interface 200 displayed at a client when a user enters a search request. As shown in fig. 2A, a user may input a search request through the user input module 210. According to some embodiments, as shown in fig. 2, a user may input text search information (e.g., "duck") through text box 211 in user input module 210 and trigger a "search" button in user input module 210 (e.g., click on the "search" button using mouse 201) to cause a client to send a user search request to a server that includes the text search information entered by the user. According to other embodiments, the user may also input search information of other modalities, such as uploading pictures, through the user input module 210.
The server generates search recommendation information and search results according to the user search request, and transmits the generated search recommendation information and search results to the client. FIG. 2B shows a schematic diagram of the user interface 200 after the client receives search recommendation information and search results.
As shown in fig. 2B, the search recommendation module 220 displays a plurality of pieces of search recommendation information (e.g., first recommendation information 221, second recommendation information 222, and third recommendation information 223) generated according to a user search request, and for example, when the user searches for "duck", the recommendation information may include "duck", "Ke Erya", "muscovy duck", "water duck", and "mandarin duck", etc.
As shown in fig. 2B, the search result module 230 displays a plurality of search results (e.g., first search content 231 and second search content 232) generated according to a user search request, for example, when the user searches for "duck", the search results are resources (e.g., pictures or web page information related to duck) searched according to "duck".
When the user selects any of the pieces of recommended information (e.g., clicks an icon corresponding to the recommended information through the mouse 201), a further search may be performed based on the selected recommended information to update the search results displayed in the search result module 230 of the client. FIG. 2C shows a schematic diagram of the user interface 200 after the user selects any of the recommended information.
As shown in fig. 2C, when the user selects the second recommendation information 222, the first search content 231 and the second search content 232 generated according to the user search request displayed by the search result module 230 are replaced with the third search content 233 and the fourth search content 234 generated according to the second recommendation information 222.
According to some embodiments, in response to the user selecting the second recommendation information 222, the client transmits a search request including the second recommendation information to the server, the server performs a search again based on the search request including the second recommendation information, and transmits search results of the search again to the client.
For example, when the user selects recommendation information "Ke Erya" generated based on "duck", the client transmits a search request based on "Ke Erya" to the server, the server performs a search again based on "Ke Erya", and the search result generated based on "duck" in the search result module 230 of the client is replaced with the search result generated based on "Ke Erya". It follows that the user may be guided to further clarify and adjust his search needs by search recommendation information generated based on the user's preliminary search request. Therefore, the search recommendation information should be comprehensive and accurate to fit the search needs of the user as much as possible.
In order to provide comprehensive and accurate search recommendation information, embodiments of the present disclosure provide a model generation method including: initializing a search tag model, wherein the search tag model comprises one or more tree structure models, each tree structure model of the search tag model corresponds to a tag type, and each node of the tree structure model is a search tag comprising at least one term; editing entries corresponding to one or more nodes of the search tag model based on the user search history; and modifying the structure of the tree structure model of the search tag model based on the user search history.
Fig. 3 shows a flow chart of a model generation method 300 according to an embodiment of the present disclosure. According to some embodiments, the method may be implemented by the server 120 described with reference to fig. 1.
At step S301, a search tag model is initialized. According to some embodiments, the search tag model includes one or more tree structure models, each tree structure model of the search tag model corresponding to a tag type, each node of the tree structure model being a search tag including at least one term.
According to some embodiments, specialized classification information may be obtained to initialize a search tag model, where the specialized classification information includes classification levels, categories in each level, and relationships between categories of different levels. For example, classification information about an organism includes organism classification levels (e.g., classification levels of "organism" include "kingdom", "phylum", "class", "order", "family", "genus" and "species"), categories in each organism classification level (e.g., two categories of "animal kingdom" and "plant kingdom" are included in a hierarchy corresponding to "kingdom"), relationships between categories of different organism classification levels (e.g., a "mammal class" in a hierarchy corresponding to "class" includes "monocular", "postzoo subclass", "bagged order" in a hierarchy corresponding to "order").
According to some embodiments, a tree structure model of the search tag model is generated from the acquired specialized classification information. According to some embodiments, each tree structure model of the search tag model corresponds to one tag type, e.g., "animal" corresponds to one tree structure model and "car" corresponds to another tree structure model.
The tree structure model may be a model representing a plurality of search tags using a tree structure. Each node in the tree structure model may be a search tag including at least one term. According to some embodiments, when a node is a search tag comprising a plurality of terms, the terms of the node include a name term indicating the name of the node and at least one alias term, e.g., for a node named "kovar," the terms "kovar," "kadi-gav," and "penbro-gav," where "kovar" is the name term, "wei-gav," "kadi-gav," and "penbro-gav" are aliased terms.
At step S303, entries corresponding to one or more nodes of the search tag model are edited based on the user search history.
According to some embodiments, the user search history is a collection of user search requests received by a server providing a search service over a period of time.
According to some embodiments, editing entries corresponding to one or more nodes of the search tag model based on the user search history includes: extracting keywords in a user search request of a user search history; selecting nodes matched with the keywords in the search tag model; and adding an entry corresponding to the keyword to the node matching the keyword in response to the node matching the keyword not including the entry corresponding to the keyword. By adding keywords in the user search request to nodes matching the keywords, synonym expansion in the search tag model is achieved.
According to further embodiments, editing entries corresponding to one or more nodes of the search tag model based on the user search history includes: responsive to the plurality of nodes in the search tag model including the same term, calculating a number of times each of the plurality of nodes is searched based on the user search history; and deleting the same term from the rest of the nodes except the node which is searched for the most times. For example, the nodes "azalea" and "azalea bird" each include the term "azalea", wherein the node "azalea" is searched the most often, the term "azalea" is deleted from the node "azalea bird", and the term "azalea" is only retained in the "azalea". The problem of word ambiguity is solved by selecting the node with the most searched times as the node corresponding to the same entry.
According to still further embodiments, editing entries corresponding to one or more nodes of the search tag model based on the user search history includes: and converting the entry corresponding to one or more nodes of the search tag model into a format required by search. According to some embodiments, the entry may be transformed to a full-half-angle transform, a case transform, a language transform, a complex-simplified transform, a nonsensical symbol removal, etc., to conform to the format required for the search.
At step S305, the structure of the tree structure model of the search tag model is modified based on the user search history.
According to some embodiments, modifying the structure of the tree structure model of the search tag model based on the user search history comprises: for each tree structure model, calculating the number of times each layer of the tree structure model is searched based on the user search history, wherein the number of times each layer of the tree structure model is searched is the number of times any node in the layer is searched; and deleting the layers in the tree structure model, the number of times of which is searched for being smaller than a predetermined number of times threshold.
According to some embodiments, calculating the number of times each layer of the tree structure model is searched comprises: for each level of the tree structure model, the number of times any node in that level is searched is calculated. That is, as long as any node in the layer is searched in a certain user search, the layer is searched in the certain user search.
According to some embodiments, for a user search request in a user search history, extracting a keyword of the user search request, and selecting a node in a search tag model that matches the keyword, wherein it is determined that the matched node was searched for by the user search request.
According to some embodiments, after deleting a layer in the tree structure model that is searched for less than a predetermined number of times threshold, the nodes in the layer below the deleted layer are connected to the nodes in the layer above the deleted layer according to the original connection relationship of the tree structure model.
For example, the first level of the tree structure model includes nodes A and B, the second level includes children nodes A 1 and A 2 of node A and children nodes B 1 and B 2 of node B, and the third level includes children nodes A 11 and A 12 of node A 1, children nodes A 21 and A 22 of node A 2, children nodes B 11 and B 12 of node B 1, And children nodes B 21 and B 22 of node B 2, then after the second layer is deleted, nodes a 11、A12、A21 and a 22 become children of node a and B 11、B12、B21 and B 22 become children of node B.
By deleting the layer with the searched times smaller than the preset times threshold in the tree structure model of the search tag model, redundant structures in the search tag model are simplified, and the speed of matching nodes in the search tag model according to the user search request is improved.
According to some embodiments, modifying the structure of the tree structure model of the search tag model based on the user search history further comprises: for each tree structure model, calculating a number of similar nodes in adjacent layers of the tree structure model; and merging adjacent layers in response to the number of similar nodes in the adjacent layers being greater than the predetermined number of nodes.
According to some embodiments, for each tree structure model, calculating the number of similar nodes in adjacent layers of the tree structure model comprises: for each node in a first layer in the adjacent layers, determining that the node and a corresponding node in a second layer are similar nodes in response to the node having similar terms to the corresponding node in the second layer; and calculating the number of nodes with corresponding similar nodes in the first layer as the number of similar nodes in the adjacent layer. According to some embodiments, the first layer is located above the second layer.
According to some embodiments, the similarity between two terms may be calculated by a cosine value similarity algorithm or an L-gram edit distance algorithm, and when the similarity between two terms exceeds a similarity threshold, the two terms are judged to be similar terms.
According to some embodiments, merging adjacent layers includes: merging similar nodes in adjacent layers, and adding the merged nodes into the merged layers; and adding nodes except similar nodes in adjacent layers to the combined layers. According to some embodiments, merging similar nodes in adjacent layers includes: and adding entries of similar nodes into the merged nodes.
In the model generation method provided by the embodiment of the disclosure, after the search tag model is initialized, the structure of the tree structure model of the search tag model is adjusted and the entry corresponding to one or more nodes of the search tag model is modified based on the user search history, so that the generated search tag model can accurately and comprehensively reflect the user search requirement.
Fig. 4 shows a schematic diagram of a search tag model 400 according to an embodiment of the present disclosure.
As shown in fig. 4, the search tag model 400 includes a first tree structure model 400a and a second tree structure model 400b, wherein the first tree structure model 400a corresponds to a first tag type (e.g., "animal") and the second tree structure model 400b corresponds to a second tag type (e.g., "car"). It should be appreciated that the number of tree structure models in search tag model 400 may be greater (e.g., 3) or less (e.g., 1).
The first tree structure model 400a and the second tree structure model 400b each include a plurality of levels, wherein each level corresponds to a classification level. For example, the first, second, and third levels 410a, 420a, and 430a of the first tree-structure model 400a correspond to "world", "class", "family", respectively, and the first, second, third, and fourth levels 410b, 420b, 430b, and 440b of the second tree-structure model 400b correspond to "car brand", "car type", "car train", "car model", respectively.
In the first tree structure model 400a and the second tree structure model 400b, the nodes in the same hierarchy are nodes under the same classification level, for example, the nodes 421a, 422a in the second hierarchy 420a of the first tree structure model 400a correspond to "mammal", "bird", respectively; the connections between nodes in different levels indicate dependencies between them, e.g., in the first tree structure model 400a, nodes 431a and 432a at the third level correspond to "feline," canine, "and are child nodes of node 421a at the second level that correspond to" mammalian.
Fig. 5 shows a flowchart of an example process of editing entries corresponding to one or more nodes of a search tag model based on a user search history in the method of fig. 3 (step S303), according to an embodiment of the present disclosure.
At step S501, keywords in a user search request of a user search history are extracted.
According to some embodiments, the keyword may be the search information itself, for example, when the user searches for the term "panda," the keyword is "panda. According to other embodiments, the keywords may be words in the search information that indicate the search target, for example, when the user searches for the term "cauchy picture," the keyword is "cauchy".
At step S503, a node matching the keyword in the search tag model is selected.
According to some embodiments, a tree structure model in a search tag model is searched for nodes that match keywords. According to some embodiments, the similarity of the term corresponding to the keyword and the node is calculated to determine whether the keyword and the node match.
At step S505, in response to the node matching the keyword not containing the term corresponding to the keyword, the term corresponding to the keyword is added to the node matching the keyword, for example, the alias term added as the node.
In an embodiment as described with reference to FIG. 5, entry information in the search tag model is enriched by "mapping" synonyms in the user's search history to matching nodes of the search tag model.
According to some embodiments, the model generation method as described in the present disclosure, further comprising, after modifying the structure of the tree structure model of the search tag model based on the user search history: in response to nodes at different levels of the same tree structure model of the search tag model having the same term, the same term in other nodes except for the lowest-level node in the nodes having the same term is deleted. For example, if the node 421a at the second level 420a and the node 432a at the third level 430a shown in fig. 4 have the same term, the same term in the node 421a at the second level 420a is deleted.
According to some embodiments, the model generation method as described in the present disclosure further includes, after modifying the structure of the tree structure model of the search tag model based on the user search history: for each layer of each tree structure model, calculating the number of times each node in the layer is searched based on the user search history; and ordering the nodes in the layer according to the searched times of each node in the layer, wherein the ordering result of the nodes in the layer is the searching heat ordering of the layer.
According to other embodiments, the model generation method as described in the present disclosure further includes, after modifying the structure of the tree structure model of the search tag model based on the user search history: for each node in each tree structure model, calculating the number of times that other nodes in the layer where the node is located co-occur with the node in the user search history based on the user search history; and ordering other nodes in the layer where the node is located according to the number of co-occurrence times, wherein the result of ordering the other nodes in the layer where the node is located is the co-occurrence ordering of the node.
In the present disclosure, if two search requests (e.g., two search requests separated by less than a certain time length) of the same user correspond to two nodes in the same layer, respectively, the two nodes are considered to co-occur in the user search history. For example, when the user searches for "cat" after searching for "dog", the nodes "canine" and "feline" are considered to co-occur in the user's search history.
The embodiment of the disclosure also provides a search recommendation method, which comprises the following steps: responding to the received user search request, selecting a node matched with the user search request in a search tag model as a hit node, wherein the search tag model is generated according to the model generation method disclosed by the disclosure; and generating search recommendation information based on the hit node.
Fig. 6 shows a flowchart of a search recommendation method 600 according to an embodiment of the present disclosure.
At step S601, in response to receiving the user search request, a node in the search tag model that matches the user search request is selected as a hit node.
According to some embodiments, in response to receiving a user search request, selecting a node in the search tag model that matches the user search request comprises: extracting keywords in the user search request in response to receiving the user search request; and selecting nodes matched with the user search request in the search tag model based on the keywords in the user search request as hit nodes, wherein the hit nodes comprise entries matched with the keywords in the user search request.
As described above, the keyword may be the search information itself or a word indicating a search target in the search information, and whether the keyword matches a node may be determined by calculating the similarity of the keyword to an entry corresponding to the node.
At step S603, search recommendation information is generated based on the hit node.
According to some embodiments, search recommendation information is generated based on the hit node and the node associated with the hit node in the search tag model, e.g., based on the hit node itself, other nodes in the layer in which the hit node is located, and/or the parent node of the hit node.
For example, when the hit node is "canine," search recommendation information is generated from the node "canine," the "feline," human, "" bear, "which are at the same level as the node" canine, "and the parent node" mammal "of the node" canine.
According to some embodiments, for each of the hit node and the nodes in the search tag model that are related to the hit node, a corresponding picture is matched for the node based on the name of the node, and the teletext information corresponding to the node is generated based on the name of the node and the matched picture.
According to some embodiments, generating search recommendation information based on the hit node includes: generating information corresponding to the hit node; generating information corresponding to other nodes of the layer in which the hit node is located based on the co-occurrence ordering of the hit node and the search heat ordering of the layer in which the hit node is located, wherein the co-occurrence ordering of the hit node is: according to the number of times that other nodes in the layer where the hit node is located and the hit node co-occur in the user search history, the result of ordering the other nodes in the layer where the hit node is located is that the search hotness of the layer where the hit node is located is ordered as follows: according to the number of times each node in the layer where the hit node is located is searched, the nodes in the layer where the hit node is located are ordered; and generating search recommendation information based on the information corresponding to the hit node and information corresponding to other nodes of the layer in which the hit node is located, wherein in the search recommendation information, the information corresponding to the hit node is located before the information corresponding to the other nodes of the layer in which the hit node is located.
Fig. 7 shows a flowchart of an example process of generating search recommendation information based on hit nodes in the method of fig. 6 (step S603) according to an embodiment of the present disclosure.
At step S701, information corresponding to the hit node is generated.
At step S703, information corresponding to other nodes of the layer in which the hit node is located is generated based on the co-occurrence ranking of the hit node and the search heat ranking of the layer in which the hit node is located.
The co-occurrence ordering of hit nodes is: and ordering the other nodes in the layer where the hit node is located according to the times of the co-occurrence of the other nodes in the layer where the hit node is located and the hit node in the user search history. The search hotness ordering of the layer where the hit node is located is: and ordering the nodes in the layer where the hit node is located according to the number of times each node in the layer where the hit node is located is searched.
At step S705, search recommendation information is generated based on the information corresponding to the hit node and the information corresponding to the other nodes of the layer in which the hit node is located, wherein in the search recommendation information, the information corresponding to the hit node is located before the information corresponding to the other nodes of the layer in which the hit node is located.
According to some embodiments, in addition to information corresponding to the hit node and information corresponding to other nodes of the layer in which the hit node is located, information of other nodes related to the hit node, for example, information corresponding to parent nodes of the hit node, may be included in the search recommendation information.
According to some embodiments, generating information corresponding to other nodes of the tier at which the hit node is located based on the co-occurrence ranking of the hit node and the search heat ranking of the tier at which the hit node is located comprises: generating information corresponding to a first predetermined number of nodes based on the co-occurrence ordering of hit nodes, wherein the first predetermined number of nodes is a previous first predetermined number of nodes in the co-occurrence ordering of hit nodes; and generating information of remaining nodes except the hit node and the first predetermined number of nodes corresponding to the layer in which the hit node is located based on the search heat ranking of the layer in which the hit node is located, wherein the information corresponding to the first predetermined number of nodes precedes the information corresponding to the remaining nodes among the information of other nodes corresponding to the layer in which the hit node is located.
For example, when the first predetermined number is 5, the search recommendation information includes, in order from front to back: information corresponding to hit nodes, information corresponding to nodes of the top five of the co-occurrence rank of hit nodes, information corresponding to remaining nodes of the layer on which hit nodes are located.
In the search recommendation method disclosed by the disclosure, the information of other nodes corresponding to the layer where the hit node is located is generated based on the co-occurrence ordering of the hit node and the search heat ordering of the layer where the hit node is located, so that a user can expand the search and adjust the search method more easily when searching, the time for the user to reach a search target is shortened, and the search scale of the user is enlarged.
Fig. 8 shows a block diagram of a model generating apparatus 800 according to an embodiment of the present disclosure.
As shown in fig. 8, the model generating apparatus 800 includes: a model initialization module 801 configured to: initializing a search tag model, wherein the search tag model comprises one or more tree structure models, each tree structure model of the search tag model corresponds to a tag type, and each node of the tree structure model is a search tag comprising at least one term; the term editing module 802 is configured to: editing entries corresponding to one or more nodes of the search tag model based on the user search history; and a structure modification module 803 configured to: the structure of the tree structure model of the search tag model is modified based on the user search history.
Fig. 9 shows a block diagram of a search recommendation apparatus 900 according to an embodiment of the present disclosure.
As shown in fig. 9, the search recommendation apparatus 900 includes: a node hit module 901 configured to: responding to a received user search request, selecting a node matched with the user search request in a search tag model as a hit node, wherein the search tag model is generated by a model generating device according to the disclosure; and a search recommendation information generation module 902 configured to: search recommendation information is generated based on the hit nodes.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.
According to embodiments of the present disclosure, there is also provided an electronic device, a readable storage medium and a computer program product.
According to an embodiment of the present disclosure, there is provided an electronic apparatus including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a model generation method and/or a search recommendation method as described in the present disclosure.
According to an embodiment of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform a model generation method and/or a search recommendation method as described in the present disclosure.
According to an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program, when being executed by a processor, implements a model generation method and/or a search recommendation method as described in the present disclosure.
Referring to fig. 10, a block diagram of a structure of an electronic device 1000 that may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 10, the apparatus 1000 includes a computing unit 1001 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a Random Access Memory (RAM) 1003. In the RAM1003, various programs and data required for the operation of the device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM1003 are connected to each other by a bus 1004. An input/output (I/O) interface 1005 is also connected to bus 1004.
Various components in device 1000 are connected to I/O interface 1005, including: an input unit 1006, an output unit 1007, a storage unit 1008, and a communication unit 1009. The input unit 1006 may be any type of device capable of inputting information to the device 1000, the input unit 1006 may receive input numeric or character information, and generate key signal inputs related to user settings and/or function control of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote control. The output unit 1007 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage unit 1008 may include, but is not limited to, magnetic disks, optical disks. Communication unit 1009 allows device 1000 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 1302.11 devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.
The computing unit 1001 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 1001 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1001 performs the various methods and processes described above, such as method 300 and/or method 600. For example, in some embodiments, method 300 and/or method 600 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 1008. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 1000 via ROM1002 and/or communication unit 1009. When the computer program is loaded into RAM 1003 and executed by computing unit 1001, one or more steps of method 300 and/or method 600 described above may be performed. Alternatively, in other embodiments, computing unit 1001 may be configured to perform method 300 and/or method 600 in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the foregoing methods, systems, and apparatus are merely exemplary embodiments or examples, and that the scope of the present invention is not limited by these embodiments or examples but only by the claims following the grant and their equivalents. Various elements of the embodiments or examples may be omitted or replaced with equivalent elements thereof. Furthermore, the steps may be performed in a different order than described in the present disclosure. Further, various elements of the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after the disclosure.
Claims (16)
1. A search recommendation method, comprising:
In response to receiving a user search request, selecting a node matched with the user search request in a search tag model as a hit node, wherein the search tag model is generated according to the following steps:
Initializing the search tag model, wherein the search tag model comprises one or more tree structure models, each tree structure model of the search tag model corresponds to a tag type, and each node of the tree structure model is a search tag comprising at least one entry;
Editing entries corresponding to one or more nodes of the search tag model based on a user search history; and
Modifying the structure of a tree structure model of the search tag model based on the user search history; and
Generating search recommendation information based on the hit node, wherein the generating search recommendation information based on the hit node includes:
generating information corresponding to the hit node;
Generating information corresponding to other nodes of the layer where the hit node is located based on the co-occurrence ordering of the hit node and the search heat ordering of the layer where the hit node is located, wherein the co-occurrence ordering of the hit node is: according to the number of times that other nodes in the layer where the hit node is located and the hit node co-occur in the user search history, the result of ordering the other nodes in the layer where the hit node is located is obtained, and the search heat ordering of the layer where the hit node is located is: according to the number of times each node in the layer where the hit node is located is searched, the nodes in the layer where the hit node is located are ordered; and
And generating the search recommendation information based on the information corresponding to the hit node and the information corresponding to other nodes of the layer where the hit node is located, wherein in the search recommendation information, the information corresponding to the hit node is located before the information corresponding to the other nodes of the layer where the hit node is located.
2. The method of claim 1, wherein the compiling the entry corresponding to the one or more nodes of the search tag model based on the user search history comprises:
Extracting keywords in a user search request of the user search history;
selecting nodes matched with the keywords in the search tag model; and
And adding an entry corresponding to the keyword to the node matched with the keyword in response to the node matched with the keyword not containing the entry corresponding to the keyword.
3. The method of claim 1, wherein the compiling the entry corresponding to the one or more nodes of the search tag model based on the user search history comprises:
responsive to a plurality of nodes in the search tag model including the same term, calculating a number of times each of the plurality of nodes is searched based on the user search history; and
And deleting the same entry from the rest nodes except the most searched nodes in the plurality of nodes.
4. The method of any of claims 1-3, wherein the modifying the structure of the tree structure model of the search tag model based on the user search history comprises:
For each tree structure model, calculating the number of times each layer of the tree structure model is searched based on the user search history, wherein the number of times each layer of the tree structure model is searched is the number of times any node in the layer is searched; and
Layers of the tree structure model that have been searched less than a predetermined number of times threshold are deleted.
5. The method of claim 4, wherein modifying the structure of the tree structure model of the search tag model based on the user search history further comprises:
for each tree structure model, calculating a number of similar nodes in adjacent layers of the tree structure model; and
And merging the adjacent layers in response to the number of similar nodes in the adjacent layers being greater than a predetermined number of nodes.
6. The method of claim 5, wherein for each tree structure model, calculating the number of similar nodes in adjacent layers of the tree structure model comprises:
For each node in a first layer of the adjacent layers, determining that the node and a corresponding node in the second layer are similar nodes in response to the node having similar terms to the corresponding node in the second layer; and
And calculating the number of nodes with corresponding similar nodes in the first layer as the number of similar nodes in the adjacent layer.
7. The method of claim 6, wherein the merging the adjacent layers comprises:
Merging similar nodes in the adjacent layers, and adding the merged nodes into the merged layers; and
And adding nodes except similar nodes in the adjacent layers into the combined layers.
8. The method of any of claims 1-3, further comprising, after the modifying the structure of the tree structure model of the search tag model based on the user search history:
And deleting the same entries in other nodes except the node with the lowest hierarchy in the nodes with the same entries in response to the nodes with different levels of the same tree structure model of the search tag model having the same entries.
9. The method of any of claims 1-3, further comprising, after the modifying the structure of the tree structure model of the search tag model based on the user search history:
for each layer of each tree structure model, calculating the number of times each node in the layer is searched based on the user search history; and
And ordering the nodes in the layer according to the searched times of each node in the layer, wherein the ordering result of the nodes in the layer is the searching heat ordering of the layer.
10. The method of any of claims 1-3, further comprising, after the modifying the structure of the tree structure model of the search tag model based on the user search history:
For each node in each tree structure model, calculating the number of times that other nodes in the layer where the node is located co-occur with the node in the user search history based on the user search history; and
And ordering other nodes in the layer where the node is located according to the co-occurrence times, wherein the result of ordering the other nodes in the layer where the node is located is the co-occurrence ordering of the node.
11. The method of claim 1, wherein the selecting, in response to receiving a user search request, a node in the search tag model that matches the user search request comprises:
extracting keywords in the user search request in response to receiving the user search request; and
And selecting a node matched with the user search request in the search tag model as the hit node based on the keywords in the user search request, wherein the hit node comprises entries matched with the keywords in the user search request.
12. The method of claim 1, wherein the generating information corresponding to other nodes of the layer at which the hit node is located based on the co-occurrence ordering of the hit node and the search heat ordering of the layer at which the hit node is located comprises:
Generating information corresponding to a first predetermined number of nodes based on the co-occurrence ordering of hit nodes, wherein the first predetermined number of nodes is a previous first predetermined number of nodes in the co-occurrence ordering of hit nodes; and
Generating information of remaining nodes except the hit node and the first predetermined number of nodes corresponding to a layer in which the hit node is located based on a search heat ranking of the layer in which the hit node is located,
Wherein, among the information corresponding to the other nodes of the layer in which the hit node is located, the information corresponding to the first predetermined number of nodes precedes the information corresponding to the remaining nodes.
13. A search recommendation apparatus comprising:
a node hit module configured to: in response to receiving a user search request, selecting a node matched with the user search request in a search tag model as a hit node, wherein the search tag model is generated according to the following steps:
Initializing the search tag model, wherein the search tag model comprises one or more tree structure models, each tree structure model of the search tag model corresponds to a tag type, and each node of the tree structure model is a search tag comprising at least one entry;
Editing entries corresponding to one or more nodes of the search tag model based on a user search history; and
Modifying the structure of a tree structure model of the search tag model based on the user search history; and
The search recommendation information generation module is configured to: generating search recommendation information based on the hit node, wherein the generating search recommendation information based on the hit node includes:
generating information corresponding to the hit node;
Generating information corresponding to other nodes of the layer where the hit node is located based on the co-occurrence ordering of the hit node and the search heat ordering of the layer where the hit node is located, wherein the co-occurrence ordering of the hit node is: according to the number of times that other nodes in the layer where the hit node is located and the hit node co-occur in the user search history, the result of ordering the other nodes in the layer where the hit node is located is obtained, and the search heat ordering of the layer where the hit node is located is: according to the number of times each node in the layer where the hit node is located is searched, the nodes in the layer where the hit node is located are ordered; and
And generating the search recommendation information based on the information corresponding to the hit node and the information corresponding to other nodes of the layer where the hit node is located, wherein in the search recommendation information, the information corresponding to the hit node is located before the information corresponding to the other nodes of the layer where the hit node is located.
14. An electronic device, comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein the method comprises the steps of
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-12.
15. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-12.
16. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the method of any of claims 1-12.
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