WO2017101317A1 - 智能推荐的全端显示方法及装置 - Google Patents
智能推荐的全端显示方法及装置 Download PDFInfo
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- the embodiments of the present invention relate to the field of video technologies, and in particular, to a smart-recommended full-end display method and apparatus.
- the intelligent recommendation system is a personalized information recommendation system that recommends information, products, and the like that are of interest to users according to the user's information needs and interests.
- the intelligent recommendation system conducts personalized calculation by studying the user's interest preference, and the system discovers the user's interest points, thereby guiding the user to discover their own information needs.
- a good recommendation system not only provides users with personalized services, but also establishes a close relationship with users, allowing users to rely on recommendations.
- the embodiment of the invention provides a smart-recommended full-end display method and device, which is used to solve the defect that the recommended content of the smart recommendation in different devices is not uniform in the prior art, and realizes the same display of the smart recommendation at the whole end.
- the embodiment of the invention provides a smart-recommended full-end display method, including:
- the user identification information is obtained, the recommendation result corresponding to the user identification information is read from the server, and the corresponding recommendation information is extracted from the recommendation result. And display the recommendation information on the corresponding device side.
- the embodiment of the invention provides a smart-recommended all-end display device, including:
- the data acquisition module is configured to acquire update data and user identification information when detecting that there is user data update on any device end;
- a calculation module configured to calculate a recommendation result according to the update data according to the pre-trained interest model, and store the recommendation result in the server corresponding to the user identification information
- a recommendation module when detecting any device request recommendation information, for acquiring user identification information, reading the recommendation result corresponding to the user identification information from the server, and extracting corresponding information from the recommendation result The recommendation information and display the recommendation information on the corresponding device end.
- the smart recommendation full-end display method and device capture user behavior by capturing the user's behavior through each device end, and the recommendation result is stored in the same server according to the interest model, supplemented by user identification information.
- the rich user behavior information can be captured more accurately through each device, and it can be used to train the user interest model, which can provide more accurate information for subsequent recommendations.
- Embodiment 1 is a technical flowchart of Embodiment 1 of the present invention.
- Embodiment 2 is a technical flowchart of Embodiment 2 of the present invention.
- Embodiment 3 is a technical flowchart of Embodiment 3 of the present invention.
- FIG. 4 is a schematic structural diagram of a device according to Embodiment 4 of the present invention.
- FIG. 5 is a schematic structural diagram of a device according to Embodiment 5 of the present invention.
- a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
- processors CPUs
- input/output interfaces network interfaces
- memory volatile and non-volatile memory
- the memory may include non-persistent memory, random access memory (RAM), and/or non-volatile memory in a computer readable medium, such as read only memory (ROM) or flash memory.
- RAM random access memory
- ROM read only memory
- Memory is an example of a computer readable medium.
- Computer readable media includes both permanent and non-persistent, removable and non-removable media.
- Information storage can be implemented by any method or technology.
- the information can be computer readable instructions, data structures, modules of programs, or other data.
- Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory. (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, Magnetic tape cartridges, magnetic tape storage or other magnetic storage devices or any other non-transportable media can be used to store information that can be accessed by a computing device.
- computer readable media does not include non-transitory computer readable media, such as modulated data signals and carrier waves.
- first device if a first device is coupled to a second device, the first device can be directly electrically coupled to the second device, or electrically coupled indirectly through other devices or coupling means. Connected to the second device.
- the description of the specification is intended to be illustrative of the preferred embodiments of the invention. The scope of protection of the application is subject to the definition of the appended claims.
- server in the present invention does not refer to a server device, in order to To ensure the load balancing of the server, the server may have multiple child nodes. Therefore, it should be understood that the server described in the embodiments of the present invention refers to a server cluster composed of multiple server devices.
- the smart recommendation method in the embodiment of the present invention is applicable to all systems that require information recommendation, such as video recommendation or product recommendation in e-commerce and other types of smart recommendation scenarios.
- FIG. 1 is a technical flowchart of Embodiment 1 of the present invention.
- an intelligent recommendation full-end display method according to an embodiment of the present invention is implemented on the server side by the following steps:
- Step 110 Acquire update data and user identification information when it is detected that there is user data update on any device end;
- the data update in the embodiment of the present invention may include an operation of the user on the existing information, a user's access footprint, a search content, a time when the user stays at a certain interface, and the like.
- a video playing device obtaining a user's selection of an existing display video, a user's viewing type, a video name of the user's search, a video classification for initiating a search, a user's sharing and rating of a certain video, etc.;
- it can be the product brand selected by the user, the type of the product, the browsing time of a certain product, the evaluation of the use, and the like.
- the purpose of obtaining the user identification information is to store the account information of the user in the server uniquely corresponding to the user data, so that when the user logs in to each device end with the same account, the generated personalized recommendation topic sequence and the recommendation information in the topic are sorted and The user's interest level is related and the data of each device is consistent. If the user is not logged in when browsing information, the user's IP address can be obtained, and the correspondence between the user data and the unique IP address can be established, thereby realizing the capture of the user interest and the related recommendation.
- the device in the embodiment of the present invention includes a web terminal, a mobile phone terminal, and a TV terminal. The device usually has a unique identification number. Therefore, the device identification number can be associated with the corresponding user through the device identification number. Establish a correspondence between user data and device identification number to capture and recommend user interest.
- Step 120 Calculate a recommendation result according to the update data, and store the recommendation result in the server corresponding to the user identification information;
- the recommendation result needs to be calculated in combination with the pre-trained interest model; according to the user
- the update data can determine the user's point of interest, that is, which features are included in the information of interest to the user, and find information that the user may be interested in according to the feature. For example, when using the video playback client, the user searches for the TV drama as a spy war drama and has a long viewing time. The background detects the user's search result, and analyzes the feature of the spy war drama to obtain the story theme and style positioning. , background age, plot and other aspects.
- the feature analysis is implemented by querying a pre-established feature tag.
- the pre-trained interest model in the server is called to perform matching, and the corresponding recommendation result is obtained, and the obtained user identification information is saved in the server corresponding to the recommendation result for subsequent information pull.
- Step 130 When it is detected that any device side takes the recommendation information, obtain the user identification information, read the recommendation result corresponding to the user identification information from the server, and pull the corresponding location from the recommendation result. Describe the recommendation information and display the recommendation information on the corresponding device side.
- the core of the embodiment of the present invention is that the user recommendation system located at each device side uses the same server data, and the server data can be carried by multiple servers, wherein the number of servers is not limited.
- the information is extracted from the recommendation results recorded in the unified server data, thereby ensuring the consistency of the recommendation result on different clients.
- the user's operation data is detected in real time, and combined with the user's account information, the user's personalized recommendation result is calculated by using the pre-established interest model, and the recommendation result is stored in a unified server, thereby realizing information recommendation for the user.
- the device has the same recommendation result on each device, it is convenient for the user to obtain information and improve the user experience.
- FIG. 2 is a technical flowchart corresponding to the second embodiment of the present invention.
- the process of training the interest model is as follows:
- the key of the information recommendation is to establish a user interest model according to the feature tag of the information to be recommended, wherein the feature tag is a feature tag of the information to be recommended, and each piece of recommendation information
- the number of corresponding feature tags is not limited, and may be fixed to a certain value, or may be determined according to the characteristics of the recommended information, but the feature tag needs to cover all the features of the information to be recommended as much as possible, so that more accurate information can be realized. recommend.
- the video feature tags may be "comedy”, “Hong Kong and Taiwan”, “adventure”, “idol”, “anime”, “anti-war”, “Republic of China”, etc., and these labels represent to some extent The actual characteristics of the video.
- the video tag also includes automatic conversion of the inherent attributes of the video, such as: starring, director, etc.
- the product tag also includes the conversion of the inherent attributes of the brand and the origin.
- Step 220 Calculate a similarity between each of the target information according to the feature tag
- the calculation of the similarity is the similarity calculation between the historical behavior of the user and the information to be recommended, and the purpose is to calculate the user interest and find the most similar partial information related to the historical behavior of the user.
- the user's historical data is small or the user's preference is relatively simple, it may generate recommendation information for many single feature tags, such as "comedy movie" or "movie starred by Huang Wei".
- the information of the user is firstly analyzed according to the historical data of the user, and the feature tag corresponding to the information of the user is searched for as a reference feature tag, and the feature tag of the target information to be recommended and the reference feature tag are calculated.
- the degree of similarity between the users is recommended to the user based on the similarity.
- the historical behavior of the user includes the information acquisition record generated by the user on each device end within a certain period of time.
- the feature tag is used as the dimension of the similarity calculation, and the similarity is calculated according to the vector distance calculation method.
- the embodiment of the present invention calculates the similarity between two feature tags by using cosine similarity.
- Cosine similarity also known as cosine similarity
- Cosine similarity is based on the principle that the similarity is evaluated by calculating the cosine of the two vectors.
- the vector is drawn into the vector space according to the coordinate values, such as the most common two-dimensional space; secondly, their angle is obtained, and the cosine value corresponding to the angle is obtained, and the cosine value can be used for characterization.
- the similarity of vectors ranges between [-1, 1], and the value approaches 1 , which means that the directions of the two vectors are closer to 0, and their directions are more consistent.
- the corresponding similarity is also higher.
- the calculation method of the similarity may also adopt the Jaccard similarity and the Pearson correlation coefficient calculation method.
- Jaccard similarity refers to the narrow Jaccard similarity.
- the Pearson product-moment correlation coefficient is used to measure the correlation (linear correlation) between two variables X and Y, and the value is between -1 and 1, where 1 indicates that the variable is completely Positive correlation, 0 means irrelevant, -1 means completely negative correlation. It evolved from a similar but slightly different idea that Carl Pearson proposed from Francis Galton in the 1880s.
- Step 230 Establish the interest model according to the similarity.
- the method for establishing interest model in the embodiment of the present invention includes collaborative filtering, matrix decomposition, graph-based model, LFM (Latent factor model) and logistic regression.
- the embodiment of the present invention does not limit the use of the algorithm, because only the continuous iteration of the algorithm can find the most suitable recommendation algorithm.
- Collaborative Filtering recommendation includes item-based CF and user-based CF.
- item-based collaborative filtering users can evaluate the similarity between items by rating different items, and make recommendations based on the similarity between items; user-based collaborative filtering, which evaluates users by rating users by different users.
- the similarity between the users is based on the similarity between users.
- collaborative filtering is mainly based on user similarity or film similarity, for example: "People who like this film also like” "like movies of your kind” are based on collaborative filtering.
- Logistic regression algorithm first needs to use the gradient descent method or stochastic gradient descent method to train the corresponding weight model of the feature tag of the information to be classified.
- this weight training is based on the collected historical behavior of the user and continuously trains to obtain the final model;
- the sigmod function is used to synthesize the relevant feature tags to obtain a correlation value between the user and the feature tag, and the value of the associated value ranges from 0 to 1. It can be abstractly understood that when the association value is between 0 and 0.5, the user's interest in the information corresponding to the feature tag is not high, and when the value is between 0.5 and 1, the user corresponds to the feature tag. Have a higher interest. Therefore, such a model with high degree of interest can be recommended, and the collaborative filtering algorithm can be used to sort the topics with high interest and high popularity. The more the previous topics, the higher the user interest. At the same time, for the same topic, a certain amount of recommendation information can currently be displayed within the theme, and how the recommendation information is displayed, For example, its ordering is also based on the recommendation model.
- the interest model is continuously updated according to the behavior of the user. After the recommendation information is displayed on the corresponding device end, the operation result of the recommendation information by the user is monitored, and the operation result is stored in the server corresponding to the user identification information to update the interest model. Each time the user's operation data and the selection of the recommendation information are new data, the training interest model is continuously updated according to the data, thereby providing users with more accurate information recommendation.
- the feature tag is established by using the recommended information
- the user interest model is established by using the recommendation algorithm, so that the user can calculate the information that matches the user's interest according to the updated data of the user, and effectively filter the information for the user.
- each target information is calculated according to the feature tag.
- Another embodiment of the similarity between the two also includes the following implementation process:
- Step 310 Create a combined label of a certain number of the feature tags according to a historical browsing behavior of the user, and generate a theme panel according to the combined label.
- a certain number of the feature tags may be generated according to the historical browsing behavior of the user, and a combination label with a certain theme may be generated and presented to the user in the form of a theme section. recommend.
- the generation rule of the combined label is set according to the historical data of the user.
- the movie with the feature label is jaundice has more viewing data, the score is high, and the number of sharing is large, and at the same time, these feature labels
- the “actor + movie type” can be used as a generation rule to generate a combination label, which can generate the following theme section: “Hyatt’s comedy movie” .
- Step 320 Calculate the similarity between the combined tags, and adjust each of the theme sections according to the similarity.
- the adjustment of the theme section includes the ordering between the target information to be recommended within the same topic section, and the adjustment of the order between the different theme sections.
- the calculation of the similarity between the topics generated by the combined label is mainly for displaying the deduplication between the theme sections on the page and the display ordering between the plurality of recommendation information in the same topic.
- the difference between the two is not big, but if the two are divided into two recommended sections to recommend to the user, the display space is wasted and the user experience is not good. Therefore, in the embodiment of the present invention, the repeated theme sections can be removed according to the similarity calculation between the two subject combination labels.
- “The comedy starring Stephen Chow” and “The funny film starring Stephen Chow” in the machine recognition, this is two themes, divided into two recommended sections.
- the two topics should be divided into the same recommended section to rationalize the display space of the recommended page and provide users with more streamlined video information recommendations.
- the recommended section of the same topic contains multiple target information to be recommended
- how to arrange the multiple target information is the key to improving the user experience.
- the similarity between the target information to be recommended and the theme of the forum in the same topic section is respectively calculated, and the target information to be recommended in the same recommended section is sorted according to the similarity level, thereby Interested information is first displayed to the user, greatly improving the quality of the recommendation.
- the adjustment of the order between the theme boards in the embodiment of the present invention is mainly adjusted according to historical data of the user within a preset time period.
- the user first calculates the information interest score of the user for each topic section in the preset time period, for example, the user browses the browsing time of each theme section, browses time, and shares information in the theme section.
- the number of times and the evaluation, the data is integrated into each of the theme sections, and after the scores are scored, each of the theme sections is displayed to the user in descending order of the scores.
- the ordering of the theme sections is continuously updated and adjusted according to the user's data, thereby achieving further mining and tracking of user interests, and to some extent, increasing the user's dependence on information recommendation.
- a plurality of single tags are used to generate a topic recommendation section, and the information is further filtered, so that the recommendation result is more in line with the user's interest; at the same time, the similarity between the combination tags is calculated.
- the merging of recommendation information with similar characteristics is realized, and the recommendation information is sorted according to the degree of interest of the user, thereby further improving the user experience.
- an intelligently recommended full-end display device mainly includes the following modules: a data acquisition module 410, a calculation module 420, a recommendation module 430, and Model training module 440.
- the data obtaining module 410 is configured to acquire update data and user identification information when detecting that there is user data update on any device end;
- the calculation module 420 is connected to the data acquisition module 410, and configured to calculate a recommendation result according to the update data, and store the recommendation result and the user identification information in a server;
- the calculation module 420 may calculate the recommendation result according to the update data in combination with the pre-trained interest model.
- the recommendation module 430 is configured to acquire user identification information when detecting any device-side request recommendation information, and read the recommendation result corresponding to the user identification information from the server, from the recommendation result. Pulling the corresponding recommendation information and displaying the recommendation information on the corresponding device end.
- the calculating module 420 is configured to perform feature analysis on the update data to obtain a corresponding feature tag, and invoke the pre-trained interest model to query the recommendation result according to the feature tag.
- model training module 440 is connected to the calculation module 420, and is configured to establish a feature tag for each target information to be recommended, and calculate a similarity between the target information according to the feature tag; The similarity establishes the interest model.
- model training module 440 is configured to establish a combination label according to a certain number of the feature labels, and calculate a similarity between the combination labels.
- the user identification information may specifically include at least one of a user account, an IP address, and a device identification number.
- the data obtaining module 410 is connected to the model training module 440, and further And after the recommendation information is displayed on the corresponding device end, monitoring an operation result of the user on the recommendation information, and storing the operation result and the user identification information in the server to update the Interest model.
- the apparatus shown in FIG. 4 can perform the method of the embodiment shown in FIG. 1 , FIG. 2 and FIG. 3 , and the implementation principle and technical effects refer to the embodiment shown in FIG. 1 , FIG. 2 and FIG. 3 , and details are not described herein again.
- FIG. 5 is a schematic structural diagram of a device according to Embodiment 5 of the present invention.
- an intelligently recommended full-end display device mainly includes a memory 501 and a processor 502.
- the memory 501 is configured to store one or more instructions, where the one or more instructions are for execution by the processor;
- the processor 502 is configured to acquire update data and user identification information when detecting that there is user data update on any device end;
- the processor 502 is further configured to perform feature analysis on the update data to obtain a corresponding feature tag; and according to the feature tag, invoke a pre-trained interest model query to query the Recommended results.
- the processor 502 is further configured to: first, use the following steps to train the interest model: establishing a feature tag for each target information to be recommended, and calculating a similarity between the target information according to the feature tag; The similarity is established to establish the interest model.
- the processor 502 when calculating the similarity between each of the target information according to the feature tag, is further configured to: establish a combination tag according to a certain number of the feature tags, and calculate the combination tag Similarity between the two.
- the user identification information specifically includes at least one of a user account, an IP address, and a device identification number.
- the processor 502 is further configured to: after displaying the recommended information on the corresponding device end, monitor an operation result of the recommended information by the user, and store the operation result and the user identification information in a corresponding manner.
- the server is used to update the interest model.
- the device shown in FIG. 5 can perform the method of the embodiment shown in FIG. 1 , FIG. 2 and FIG. 3 , and the implementation principle and technical effects refer to the embodiments shown in FIG. 1 , FIG. 2 and FIG. 3 , and details are not described herein again.
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Abstract
一种智能推荐的全端显示方法及装置,该方法包括:当检测到任意设备端有用户数据更新时,获取更新数据以及用户识别信息;根据所述更新数据结合预先训练的兴趣模型计算推荐结果,并将所述推荐结果与用户识别信息对应存入服务器中;当检测到任意设备端请求推荐信息时,获取用户识别信息,从所述服务器中读取与所述用户识别信息对应的所述推荐结果,从所述推荐结果中拉取相应的所述推荐信息,并将所述推荐信息显示在相应的设备端。该方法实现了智能推荐在各个设备端的统一,方便用户在全端获取信息。
Description
交叉引用
本申请引用于2015年12月14日递交的名称为“智能推荐的全端显示方法及装置”的第2015109261586号中国专利申请,其通过引用被全部并入本申请。
本发明实施例涉及视频技术领域,尤其涉及一种智能推荐的全端显示方法及装置。
互联网的出现和普及给用户带来了大量的信息,满足了用户在信息时代对信息的需求,但随着网络的迅速发展而带来的网上信息量的大幅增长,使得用户在面对大量信息时无法从中获得对自己真正有用的那部分信息,因此导致用户对信息的使用效率反而降低了,即所谓的信息超载。例如,随着电子商务规模的不断扩大,商品个数和种类快速增长,顾客需要花费大量的时间才能找到自己想买的商品。这种浏览大量无关的信息和产品过程无疑会使淹没在信息超载问题中的消费者不断流失。
目前,智能推荐***的出现是解决信息超载问题的一个非常有潜力的方法。智能推荐***是一个根据用户的信息需求、兴趣等,将用户感兴趣的信息、产品等推荐给用户的个性化信息推荐***。与传统的搜索引擎相比,智能推荐***通过研究用户的兴趣偏好,进行个性化计算,由***发现用户的兴趣点,从而引导用户发现自己的信息需求。一个好的推荐***不仅能为用户提供个性化的服务,还能和用户之间建立密切关系,让用户对推荐产生依赖。
随着智能设备的种类的逐渐增多,智能推荐在更多设备端上有了用武之
地,但是由于智能推荐***应用在各端的相关位置,对于同一用户,位于不同设备端的智能推荐结果不同,因此很难准确捕捉用户行为。若是不能准确捕捉用户的行为,则会导致在用户兴趣度模型建立的时候产生偏差,从而影响智能推荐***的质量。比如,乐视集团目前涵盖PC,APP,TV等端,对于视频的智能推荐,若是各端的推荐内容不统一将会影响用户观影体验,甚至有可能在一定程度上影响用户对视频的依赖性。
发明内容
本发明实施例提供一种智能推荐的全端显示方法及装置,用以解决现有技术中智能推荐在不同设备端推荐内容不统一的缺陷,实现了智能推荐在全端的同一显示。
本发明实施例提供一种智能推荐的全端显示方法,包括:
当检测到任意设备端有用户数据更新时,获取更新数据以及用户识别信息;
根据所述更新数据计算推荐结果,并将所述推荐结果与用户识别信息对应存入服务器中;
当检测到任意设备端请求推荐信息时,获取用户识别信息,从所述服务器中读取与所述用户识别信息对应的所述推荐结果,从所述推荐结果中拉取相应的所述推荐信息,并将所述推荐信息显示在相应的设备端。
本发明实施例提供一种智能推荐的全端显示装置,包括:
数据获取模块,当检测到任意设备端有用户数据更新时,用于获取更新数据以及用户识别信息;
计算模块,用于根据所述更新数据结合预先训练的兴趣模型计算推荐结果,并将所述推荐结果与用户识别信息对应存入服务器中;
推荐模块,当检测到任意设备端请求推荐信息时,用于获取用户识别信息,从所述服务器中读取与所述用户识别信息对应的所述推荐结果,从所述推荐结果中拉取相应的所述推荐信息,并将所述推荐信息显示在相应的设备
端。
本发明实施例提供的智能推荐的全端显示方法及装置,通过各个设备端对用户的行为进行捕捉从而建立用户兴趣模型,并根据兴趣模型将推荐结果存在同一服务器中,辅以用户识别信息,当通过不同设备端向用户进行信息推荐时,从同一服务器拉取数据,由此实现了智能推荐在各个端的统一,方便用户在全端获取信息。与此同时,通过各个设备端可以更准确地捕捉丰富的用户行为信息,将之用于训练用户兴趣模型,可以为后续的推荐提供更加准确的信息。
此处所说明的附图用来提供对本发明的进一步理解,构成本申请的一部分,本发明的示意性实施例及其说明用于解释本发明,并不构成对本发明的不当限定。在附图中:
图1为本发明实施例一的技术流程图;
图2为本发明实施例二的技术流程图;
图3是本发明实施例三的技术流程图;
图4为本发明实施例四的装置结构示意图;
图5为本发明实施例五的设备结构示意图。
为使本发明的目的、技术方案和优点更加清楚,以下结合附图及具体实施例,对本发明作进一步地详细说明。在一个典型的配置中,计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
内存可能包括计算机可读介质中的非永久性存储器,随机存取存储器(RAM)和/或非易失性内存等形式,如只读存储器(ROM)或闪存(flash RAM)。内存是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括,但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带,磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质,可用于存储可以被计算设备访问的信息。按照本文中的界定,计算机可读介质不包括非暂存电脑可读媒体(transitory media),如调制的数据信号和载波。
如在说明书及权利要求当中使用了某些词汇来指称特定组件。本领域技术人员应可理解,硬件制造商可能会用不同名词来称呼同一个组件。本说明书及权利要求并不以名称的差异来作为区分组件的方式,而是以组件在功能上的差异来作为区分的准则。如在通篇说明书及权利要求当中所提及的“包含”为一开放式用语,故应解释成“包含但不限定于”。“大致”是指在可接收的误差范围内,本领域技术人员能够在一定误差范围内解决所述技术问题,基本达到所述技术效果。此外,“耦接”一词在此包含任何直接及间接的电性耦接手段。因此,若文中描述一第一装置耦接于一第二装置,则代表所述第一装置可直接电性耦接于所述第二装置,或通过其他装置或耦接手段间接地电性耦接至所述第二装置。说明书后续描述为实施本申请的较佳实施方式,然所述描述乃以说明本申请的一般原则为目的,并非用以限定本申请的范围。本申请的保护范围当视所附权利要求所界定者为准。
还需要说明的是,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的商品或者***不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种商品或者***所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括所述要素的商品或者***中还存在另外的相同要素。
需要说明的是,本发明所述的“服务器”并非指一台服务器设备,为了
保证服务器的负载均衡,服务器可以有多个子节点构成,因此应当理解,本发明各实施例中所述的服务器是指由多台服务器设备构成的服务器集群。
本发明实施例的智能推荐方法,适用于所有需要信息推荐的***,如视频推荐或是电子商务中商品推荐以及其他类型的智能推荐场景。
实施例一
图1是本发明实施例一的技术流程图,结合图1,本发明实施例一种智能推荐的全端显示方法,在服务器端主要由以下的步骤实现:
步骤110:当检测到任意设备端有用户数据更新时,获取更新数据以及用户识别信息;
本发明实施例中的所述数据更新可以包括用户对现有信息的操作、用户的访问足迹、搜索内容、用户在某一界面停留的时间等。例如在视频播放设备中,获取用户对现有展示视频的选择情况,用户的观看类型以及用户发起搜索的视频名称以及发起搜索的视频分类,用户对某部影片的分享及评分情况等;在电子商务中,可以是用户选择的商品品牌、商品类型、对某一商品的浏览时间、使用评价等等。
获取用户识别信息目的在于,将用户的账号信息与用户数据唯一对应地保存在服务器中,从而,当用户用同一账号登录各设备端时,生成的个性化推荐主题顺序、主题内推荐信息排序与用户的兴趣度相关并且各设备端数据保持一致。若用户在浏览信息时处于未登录状态或,则可获取用户的IP地址,建立用户数据与唯一IP地址的对应关系,从而也能实现用户兴趣的捕捉以及相关推荐。本发明实施例中的所述设备端包括Web端,手机端以及TV端等,这些设备通常有一个唯一的识别号,因此,本发明实施例还可以通过设备识别号与相应的用户关联,通过建立用户数据与设备识别号的对应关系进行用户兴趣的捕捉与推荐。
步骤120:根据所述更新数据计算推荐结果,并将所述推荐结果与用户识别信息对应存入服务器中;
可选地,需要结合预先训练的兴趣模型计算推荐结果;根据用户的所述
更新数据可以判断出用户的兴趣点所在,即用户感兴趣的信息包括哪些特征,并跟据所述特征寻找用户可能会感兴趣的信息。例如,在使用视频播放客户端时,用户搜索电视剧为某一谍战剧且观看时间较长,后台检测到用户的搜索结果,并对这部谍战剧进行特征分析,得到故事主题,风格定位,背景年代,情节等方面。
本发明实施例中,所述特征分析通过查询预先建立的特征标签实现。根据所述查询到的特征标签,调用服务器中预先训练的兴趣模型进行匹配,得到相应的推荐结果,并将获取到的用户识别信息与推荐结果对应地保存在服务器中用于后续信息拉取。
步骤130:当检测到任意设备端取推荐信息时,获取用户识别信息,从所述服务器中读取与所述用户识别信息对应的所述推荐结果,从所述推荐结果中拉取相应的所述推荐信息,并将所述推荐信息显示在相应的设备端。
本发明实施例的核心在于,位于各设备端的用户推荐***使用同一服务器数据,而这些服务器数据可以由多个服务器来承载,其中服务器的数量不做限制。当用户需要读取推荐结果时,不管是Web端,App,还是TV端,均从统一的服务器数据中记录的推荐结果中拉取信息,从而保证推荐结果在不同客户端的一致性。本实施例中,实时检测用户的操作数据,并与用户的账号信息结合,使用预先建立的兴趣模型计算用户个性化的推荐结果,将推荐结果存入统一的服务器,实现了为用户进行信息推荐时,在各个设备端有相同的推荐结果,方便用户对于信息的获取,提升了用户体验。
实施例二
图2是本发明实施例二对应的技术流程图,结合图2,本发明实施例一种智能推荐的全端显示方法中,训练兴趣模型的过程如下:
步骤:210:对每一个待推荐的目标信息建立特征标签;
本发明实施例中,信息推荐的关键在于根据待推荐信息的特征标签建立用户兴趣模型,其中,特征标签是待推荐信息的特征标记,每一条推荐信息
对应的特征标签的数量不做限制,可以固定为某个数值,也可以根据推荐信息的特征决定,但是这一特征标签需尽可能覆盖待推荐信息的所有特征,这样才能够实现更加精确的信息推荐。例如,在视频信息分类中,视频特征标签可以是“喜剧”、“港台”、“冒险”、“偶像”、“动漫”、“抗战”、“民国”等等,这些标签一定程度上代表了视频的实际特征。当然视频标签还包括视频固有属性的自动转化,例如:主演、导演等,同样对于某一商品,其商品标签也包括其品牌、产地等固有属性的转化。
步骤220:根据所述特征标签计算每个所述目标信息之间的相似度;
本实施例中,相似度的计算为用户历史行为与待推荐信息之间的相似度计算,其目的在于,计算用户兴趣,找到与用户历史行为相关的最相似的部分信息。若用户的历史数据较少或者用户爱好较为单一,则可能产生许多单特征标签的推荐信息,如“喜剧电影”或者“黄渤主演的电影”。具体地,首先根据用户的历史数据分析得到用户感兴趣的信息,查找所述用户感兴趣的信息对应的特征标签作为参考特征标签,计算所述待推荐目标信息的特征标签与所述参考特征标签之间的相似度,根据所述相似度向用户推荐可能感兴趣的信息。其中,用户的历史行为包括用户在一段时间范围内在各个设备端的产生的信息获取记录。
本发明实施例中以所述特征标签作为所述相似度计算的维度,根据向量距离计算方法计算所述相似度。
具体地,本发明实施例采用余弦相似度计算两个特征标签之间的相似度。余弦相似度,又称为余弦相似性,其原理在于通过计算两个向量的夹角余弦值来评估他们的相似度。首先将向量根据坐标值,绘制到向量空间中,如最常见的二维空间;其次求得他们的夹角,并得出夹角对应的余弦值,此余弦值就可以用来表征,这两个向量的相似性。余弦值的范围在[-1,1]之间,值越趋近于1,代表两个向量的方向越趋近于0,他们的方向更加一致。相应的相似度也越高。
本实施例中,相似度的计算方法也可以采用Jaccard相似度以及皮尔森相关系数计算方法。
所述Jaccard相似度是指狭义Jaccard相似。对集合A和B,Jaccard相似度计算如下:Jaccard(A,B)=|A intersect B|/|A union B|
相似度数值在[0,1]之间,当A==B的时候,Jaccard(A,B)为1。
所述皮尔森相关系数(Pearson product-moment correlation coefficient),用于度量两个变量X和Y之间的相关(线性相关),其值介于-1与1之间,其中,1表示变量完全正相关,0表示无关,-1表示完全负相关。它是由卡尔·皮尔逊从弗朗西斯·高尔顿在19世纪80年代提出的一个相似却又稍有不同的想法演变而来的。
步骤230:根据所述相似度建立所述兴趣模型。本发明实施例采用兴趣模型建立方法包括协同过滤,矩阵分解,基于图的模型,LFM(Latent factor model,隐语义模型)以及逻辑回归等。本发明实施例对算法的使用不做限制,因只有通过算法的不断迭代才能找到最适合的推荐算法。
协同过滤(Collaborative Filtering recommendation,简称CF)包括item-based CF以及user-based CF。基于item的协同过滤,通过用户对不同item的评分来评测item之间的相似性,基于item之间的相似性做出推荐;基于user的协同过滤,通过不同用户对item的评分来评测用户之间的相似性,基于用户之间的相似性做出推荐。例如,在视频推荐***中,协同过滤主要是基于用户相似性或者影片相似性,例:“喜欢这部片的人也喜欢”“你喜欢的同类影片”都是基于协同过滤。逻辑回归算法首先需要利用梯度下降法或者随机梯度下降法等其他方法训练待分类信息特征标签的相应权重模型,当然这个权重训练是以收集到的用户历史行为为基准不断训练得到最终模型;然后再利用sigmod函数综合相关特征标签得到一个用户与特征标签的关联值,所述关联值的取值范围在0到1之间。可以抽象的理解为当所述关联值为0到0.5之间时,表示用户对此特征标签对应信息的兴趣度不高,而当值在0.5到1之间时表示用户对此特征标签对应信息有较高的兴趣。因此可以推荐出此类兴趣度较高的模型,并且可以利用协同过滤算法对兴趣度以及热度较高的主题排序,越在前面的主题,用户兴趣度越高。与此同时,对于同一个主题,主题内目前可展示一定数量的推荐信息,所述推荐信息如何展示,
例如其前后排序也是基于推荐模型得到的。
需要说明的是,本发明实施例中,兴趣模型根据用户的行为不断更新。在推荐信息显示在相应的设备端之后,监测用户对所述推荐信息的操作结果,并将所述操作结果与所述用户识别信息对应存入所述服务器中用以更新所述兴趣模型。每一次用户的操作数据以及对推荐信息的选择情况都是新的数据,根据这些数据不断地更新训练兴趣模型,从而为用户提供更加个精准性化的信息推荐。
本实施例中,通过对待推荐的信息建立特征标签并采用推荐算法建立用户兴趣模型,从而可以根据用户的更新数据为用户计算出符合用户兴趣的信息,有效地为用户进行信息过滤。
实施例三
图3是本发明实施例三对应的技术流程图,结合图3,本发明实施例一种智能推荐的全端显示方法中,训练兴趣模型中,根据所述特征标签计算每个所述目标信息之间的相似度的另一实施例还包括如下的实施过程:
步骤310:根据用户的历史浏览行为将一定数量的所述特征标签建立组合标签,并根据所述组合标签生成主题板块;
需要说明的是,本发明实施例中,若是用户的历史数据较多,可以根据用户的历史浏览行为将一定数量的所述特征标签生成有一定主题的组合标签,并以主题版块的形式向用户推荐。组合标签的生成规则根据用户的历史数据设置,例如,对于某一用户在观看影片时,对特征标签为黄渤的电影观看数据较多,评分高且分享次数多,与此同时,这些特征标签为黄渤的电影还同时具有“喜剧”或是“搞笑”等特征标签,则可“演员+电影类型”作为生成规则生成组合标签,这样可以生成如下主题版块:“黄渤主演的喜剧电影”。
步骤320:计算所述组合标签之间的相似度,并根据所述相似度调整各所述主题板块。
本步骤中,对所述主题板块的调整包括同一主题板块之内,各个待推荐的目标信息之间的排序以及各个不同的主题板块之间排序的调整。
本步骤中,计算组合标签生成的主题之间的相似度计算主要是为了显示页面上各主题版块之间的去重以及同一主题内多个推荐信息之间的显示排序。例如“周星驰主演的喜剧片”和“周星驰主演的搞笑片”,二者差别并不大,但是若将二者分为两个推荐版块向用户进行推荐,则浪费了显示空间且用户使用体验不佳。因此,本发明实施例中,可以根据两个主题组合标签之间的相似度计算,去除重复的主题版块。“周星驰主演的喜剧片”和“周星驰主演的搞笑片”,在机器识别时,这是两个主题,被分成两个推荐版块。但是经过相似性计算,这两个主题应当被划分在同一个推荐版块中,从而合理化推荐页面的显示空间并且能够为用户提供更精简的视频信息推荐。
除此之外,若是同一主题的推荐版块中包含多个待推荐的目标信息,如何排列这多个目标信息是提升用户使用体验的关键。本发明实施例中,分别计算同一主题版块内的多个待推荐的目标信息与版块主题的相似度,并将这同一推荐版块中的待推荐目标信息按照相似度的高低排序,从而将用户最感兴趣的信息首先展示给用户,极大提升了推荐的质量。需要说明的是,本发明实施例中主题板块之间排序的调整,主要根据用户在预设时间段内的历史数据进行调整。具体实现时,首先检测在所述预设时间段内,计算用户对各个主题板块的信息兴趣度得分,比如,通过用户对各主题板块的浏览量、浏览时间、对这一主题板块内信息分享次数以及评价,综合这些数据对每个所述主题版块打分,得到打分成绩后,按照打分成绩降序将每个所述主题板块展示给用户。当然,需要说明的是,所述主题板块的排序根据用户的数据不断更新调整,从而实现了对用户兴趣的进一步挖掘跟踪,并在一定程度上增加了用户对信息推荐的依赖性。
本实施例中,根据用户的历史数据,将多个单一标签生成主题推荐版块,对信息进行进一步地过滤,使得推荐结果更加符合用户的兴趣;与此同时,通过计算组合标签之间的相似度,实现了特征相似的推荐信息之间的合并以及根据用户的兴趣度将推荐信息进行排序,进一步提升了用户体验。
实施例四
图4是本发明实施例四的装置结构示意图,结合图4,本发明实施例一种智能推荐的全端显示装置,主要包括如下的模块:数据获取模块410、计算模块420、推荐模块430以及模型训练模块440。
所述数据获取模块410,当检测到任意设备端有用户数据更新时,用于获取更新数据以及用户识别信息;
所述计算模块420,与所述数据获取模块410相连接,用于根据所述更新数据计算推荐结果,并将所述推荐结果与用户识别信息对应存入服务器中;
可选地,计算模块420可以根据所述更新数据,结合预先训练的兴趣模型计算推荐结果。
所述推荐模块430,当检测到任意设备端请求推荐信息时,用于获取用户识别信息,从所述服务器中读取与所述用户识别信息对应的所述推荐结果,从所述推荐结果中拉取相应的所述推荐信息,并将所述推荐信息显示在相应的设备端。
进一步地,所述计算模块420,用于:对所述更新数据进行特征分析获取相应的特征标签;根据所述特征标签,调用预先训练的兴趣模型查询所述推荐结果。
进一步地,所述模型训练模块440与所述计算模块420相连,用于对每一个待推荐的目标信息建立特征标签,根据所述特征标签计算所述目标信息之间的相似度;根据所述相似度建立所述兴趣模型。
进一步地,所述模型训练模块440用于根据一定数量的所述特征标签建立组合标签,计算所述组合标签之间的相似度。
进一步地,所述用户识别信息具体可以包括:用户账号、IP地址、设备识别号中的至少一个。
进一步地,所述数据获取模块410与所述模型训练模块440相连接,还
用于在所述推荐信息显示在相应的设备端之后,监测用户对所述推荐信息的操作结果,并将所述操作结果与所述用户识别信息对应存入所述服务器中用以更新所述兴趣模型。
图4所示装置可以执行图1、图2以及图3所示实施例的方法,实现原理和技术效果参考图1、图2以及图3所示实施例,不再赘述。
实施例五
图5是本发明实施例五的设备结构示意图,结合图5,本发明实施例一种智能推荐的全端显示设备,主要包括内存501以及处理器502。
其中,所述内存501用于存储一条或多条指令,其中,所述一条或多条指令以供所述处理器调用执行;
所述处理器502,用于当检测到任意设备端有用户数据更新时,获取更新数据以及用户识别信息;
根据所述更新数据获取推荐结果,并将所述推荐结果与所述用户识别信息对应存入服务器中;
当检测到任意设备端请求推荐信息时,获取所述用户识别信息,从所述服务器中读取与所述用户识别信息对应的所述推荐结果,从所述推荐结果中拉取相应的所述推荐信息,并将所述推荐信息显示在相应的设备端。
当根据所述更新数据获取推荐结果时,所述处理器502,进一步用于,对所述更新数据进行特征分析获取相应的特征标签;根据所述特征标签,调用预先训练的兴趣模型查询所述推荐结果。
所述处理器502,进一步用于,预先采用如下步骤训练所述兴趣模型:对每一个待推荐的目标信息建立特征标签,根据所述特征标签计算所述目标信息之间的相似度;根据所述相似度建立所述兴趣模型。
具体的,根据所述特征标签计算每个所述目标信息之间的相似度时,所述处理器502,进一步用于:根据一定数量的所述特征标签建立组合标签,计算所述组合标签之间的相似度。
具体的,所述用户识别信息具体包括:用户账号、IP地址、设备识别号中的至少一个。
所述处理器502,进一步用于,将所述推荐信息显示在相应的设备端之后,监测用户对所述推荐信息的操作结果,并将所述操作结果与所述用户识别信息对应存入所述服务器中用以更新所述兴趣模型。
图5所示设备可以执行图1、图2以及图3所示实施例的方法,实现原理和技术效果参考图1、图2以及图3所示实施例,不再赘述。
Claims (12)
- 一种智能推荐的全端显示方法,其特征在于,包括如下的步骤:当检测到任意设备端有用户数据更新时,获取更新数据以及用户识别信息;根据所述更新数据获取推荐结果,并将所述推荐结果与所述用户识别信息对应存入服务器中;当检测到任意设备端请求推荐信息时,获取所述用户识别信息,从所述服务器中读取与所述用户识别信息对应的所述推荐结果,从所述推荐结果中拉取相应的所述推荐信息,并将所述推荐信息显示在相应的设备端。
- 根据权利要求1所述的方法,其特征在于,根据所述更新数据获取推荐结果,包括:对所述更新数据进行特征分析获取相应的特征标签;根据所述特征标签,调用预先训练的兴趣模型查询所述推荐结果。
- 根据权利要求2所述的方法,其特征在于,所述方法包括,预先采用如下步骤训练所述兴趣模型:对每一个待推荐的目标信息建立特征标签,根据所述特征标签计算所述目标信息之间的相似度;根据所述相似度建立所述兴趣模型。
- 根据权利要求3所述的方法,其特征在于,根据所述特征标签计算每个所述目标信息之间的相似度,进一步包括:根据一定数量的所述特征标签建立组合标签,计算所述组合标签之间的相似度。
- 根据权利要求1所述的方法,其特征在于,所述用户识别信息具体包括:用户账号、IP地址、设备识别号中的至少一个。
- 根据权利要求1所述的方法,其特征在于,所述方法进一步包括:将所述推荐信息显示在相应的设备端之后,监测用户对所述推荐信息的操作结果,并将所述操作结果与所述用户识别信息对应存入所述服务器中用以更新所述兴趣模型。
- 一种智能推荐的全端显示装置,其特征在于,包括如下的模块:数据获取模块,当检测到任意设备端有用户数据更新时,用于获取更新数据以及用户识别信息;计算模块,用于根据所述更新数据计算推荐结果,并将所述推荐结果与所述用户识别信息对应存入服务器中;推荐模块,当检测到任意设备端请求推荐信息时,用于获取所述用户识别信息,从所述服务器中读取与所述用户识别信息对应的所述推荐结果,从所述推荐结果中拉取相应的所述推荐信息,并将所述推荐信息显示在相应的设备端。
- 根据权利要求7所述的装置,其特征在于,所述计算模块,用于:对所述更新数据进行特征分析获取相应的特征标签;根据所述特征标签,调用预先训练的兴趣模型查询所述推荐结果。
- 根据权利要求8所述的装置,其特征在于,所述装置还包括模型训练模块,所述模型训练模块用于:对每一个待推荐的目标信息建立特征标签,根据所述特征标签计算所述目标信息之间的相似度;根据用户的历史数据以及所述相似度建立所述兴趣模型。
- 根据权利要求9所述的装置,其特征在于,所述模型训练模块用于:根据一定数量的所述特征标签建立组合标签,计算所述组合标签之间的相似度。
- 根据权利要求7所述的装置,其特征在于,所述用户识别信息 具体包括:用户账号、IP地址、设备识别号中的至少一个。
- 根据权利要求7所述的装置,其特征在于,所述数据获取模块还用于:在所述推荐信息显示在相应的设备端之后,监测用户对所述推荐信息的操作结果,并将所述操作结果与所述用户识别信息对应存入所述服务器中用以更新所述兴趣模型。
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