CN112330427A - Method, electronic device and storage medium for commodity sorting - Google Patents
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Abstract
Embodiments of the present disclosure relate to a method, an electronic device, and a computer storage medium for commodity sorting, and relate to the field of information processing. According to the method, a plurality of heat values of a plurality of commodities at a current time are generated based on a plurality of shelf-loading times, a plurality of initial heat values and a time decay model of the commodities; generating a plurality of value sets of a plurality of commodities on a plurality of predetermined indexes on the basis of historical data of the commodities within a predetermined time interval from the current time; generating a plurality of entropies associated with a plurality of predetermined metrics based on a plurality of sets of values; generating a plurality of weights associated with a plurality of predetermined metrics based on the plurality of entropies; generating a plurality of interactive behavior values associated with a plurality of commodities based on the plurality of weights and the plurality of numerical value sets; generating a plurality of total heat values based on the plurality of heat values and the plurality of interactive behavior values; and generating a first ranking result of the plurality of commodities at the current time based on the plurality of total heat values. Thus, the commodity heat value can be determined in multiple dimensions and sorted.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of information processing, and more particularly, to a method, electronic device, and computer storage medium for merchandise sorting.
Background
The commodity sequencing of the existing commodity list page is mainly arranged according to single-dimensional degrees such as sales volume, price, commodity shelf time and the like, and the problems are caused: 1) only sorting according to sales volume can cause the Martian effect of commodity sales volume, commodities which are sold well at first are sold well, and commodities which are not sold well at first and new commodities have a chance again; 2) sorting according to price only can cause the display chance of new products or popular products to be lacked; 3) according to the commodity shelf time sequence, the new goods on shelf are all arranged in front, the good selling goods lack the display opportunity, and the sequence can not reflect the requirement of the user.
Disclosure of Invention
A method, an electronic device and a computer storage medium for commodity ranking are provided, which can determine commodity heat value for ranking in multiple dimensions.
According to a first aspect of the present disclosure, a method for merchandise sorting is provided. The method comprises the following steps: generating a plurality of heat values of the plurality of commodities at a current time based on a plurality of shelf-loading times, a plurality of initial heat values and a time decay model of the plurality of commodities; generating a plurality of value sets of the plurality of commodities on a plurality of predetermined indexes based on historical data of the commodities within a predetermined time interval from the current time, wherein each value set in the value sets comprises a plurality of values of the associated commodity on the predetermined indexes; generating a plurality of entropies associated with a plurality of predetermined metrics based on a plurality of sets of values; generating a plurality of weights associated with a plurality of predetermined metrics based on the plurality of entropies; generating a plurality of interactive behavior values associated with a plurality of commodities based on the plurality of weights and the plurality of numerical value sets; generating a plurality of total heat values associated with the plurality of commodities based on the plurality of heat values and the plurality of interactive behavior values; and generating a first ranking result of the plurality of items at the current time for presentation based on the plurality of total heat value.
According to a second aspect of the present disclosure, an electronic device is provided. The electronic device includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to perform the method according to the first aspect.
In a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements a method according to the first aspect of the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements.
FIG. 1 is a schematic diagram of an information handling environment 100 according to an embodiment of the present disclosure.
Fig. 2 is a schematic diagram of a method 200 for item ordering according to an embodiment of the present disclosure.
FIG. 3 is a schematic diagram of a method 300 for generating a plurality of heat magnitude values, according to an embodiment of the present disclosure.
Fig. 4 is a schematic diagram of a method 400 for determining a total calorific value from a second ranking result, according to an embodiment of the present disclosure.
Fig. 5 is a schematic diagram of a method 500 for generating a plurality of entropies, according to an embodiment of the present disclosure.
FIG. 6 is a block diagram of an electronic device for implementing a method for merchandise sorting according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those 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 and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.
As described above, the commodity list page is a collective page of commodities, and is large in number of products and information, and how to make the user see the most needed commodities after arriving at the commodity list page is important. For the mall list page, we consider both the real needs of the user and the potential sales opportunities. On one hand, more display opportunities are given to the commodities approved by the user as much as possible; on the other hand, new products with sales potential are given sufficient display opportunities.
To address, at least in part, one or more of the above problems, as well as other potential problems, example embodiments of the present disclosure propose a scheme for commodity ordering. In the scheme, a plurality of heat value of a plurality of commodities at the current time are generated based on a plurality of shelf-loading time, a plurality of initial heat value and a time attenuation model of the commodities; generating a plurality of value sets of the plurality of commodities on a plurality of predetermined indexes based on historical data of the commodities within a predetermined time interval from the current time, wherein each value set in the value sets comprises a plurality of values of the associated commodity on the predetermined indexes; generating a plurality of entropies associated with a plurality of predetermined metrics based on a plurality of sets of values; generating a plurality of weights associated with a plurality of predetermined metrics based on the plurality of entropies; generating a plurality of interactive behavior values associated with a plurality of commodities based on the plurality of weights and the plurality of numerical value sets; generating a plurality of total heat values associated with the plurality of commodities based on the plurality of heat values and the plurality of interactive behavior values; and generating a first ranking result of the plurality of items at the current time for presentation based on the plurality of total heat value. In this way, the commodity heat value can be determined in multiple dimensions for sorting.
Hereinafter, specific examples of the present scheme will be described in more detail with reference to the accompanying drawings.
FIG. 1 shows a schematic diagram of an example of an information processing environment 100, according to an embodiment of the present disclosure. Information handling environment 100 may include a computing device 110, a plurality of items 120-1 through 120-m, historical data 130 for the plurality of items within a predetermined time interval from a current time, and a first ordering result 140 for the plurality of items.
The computing device 110 includes, for example, but is not limited to, a server computer, a multiprocessor system, a mainframe computer, a distributed computing environment including any of the above systems or devices, and the like. In some embodiments, the computing device 110 may have one or more processing units, including special purpose processing units such as image processing units GPU, field programmable gate arrays FPGA, and application specific integrated circuits ASIC, and general purpose processing units such as central processing units CPU.
The computing device 110 is configured to generate a plurality of heat values for the plurality of items 120-1 to 120-m at a current time based on a plurality of time-to-shelf, a plurality of initial heat values, and a time decay model for the plurality of items 120-1 to 120-m; generating a plurality of value sets of the plurality of commodities 120-1 to 120-m on a plurality of predetermined indexes based on historical data 130 of the commodities 120-1 to 120-m within a predetermined time interval from the current time, wherein each value set in the plurality of value sets comprises a plurality of values of the associated commodity on the predetermined indexes; generating a plurality of entropies associated with a plurality of predetermined metrics based on a plurality of sets of values; generating a plurality of weights associated with a plurality of predetermined metrics based on the plurality of entropies; generating a plurality of interactive behavior values associated with a plurality of commodities based on the plurality of weights and the plurality of numerical value sets; generating a plurality of total heat values associated with the plurality of commodities based on the plurality of heat values and the plurality of interactive behavior values; and generating a first ranking result 140 of the plurality of items at the current time for presentation based on the plurality of total heat values.
Therefore, the commodity heat value can be comprehensively calculated in multiple dimensions to serve as a commodity sequencing basis, so that new commodities or potential commodities have more exposure opportunities, and mass commodities can be better seen by users.
FIG. 2 shows a flow diagram of a method 200 for item ordering according to an embodiment of the present disclosure. For example, the method 200 may be performed by the computing device 110 as shown in FIG. 1. It should be understood that method 200 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the present disclosure is not limited in this respect.
At block 202, the computing device 110 generates a plurality of heat values for the plurality of items 120-1 through 120-m at a current time based on the plurality of time-to-shelf, the plurality of initial heat values, and the time decay model for the plurality of items 120-1 through 120-m. The method for generating the plurality of heat magnitude values will be described in detail below in conjunction with fig. 3.
At block 204, the computing device 110 generates a plurality of sets of values for the plurality of items 120-1 through 120-m on a plurality of predetermined metrics based on the historical data 130 for the plurality of items 120-1 through 120-m over a predetermined time interval from the current time. Each value set of the plurality of value sets includes a plurality of values of the associated item on a plurality of predetermined criteria.
The predetermined time interval includes, for example, but is not limited to, 30 days.
The plurality of predetermined metrics include, for example, but not limited to, the number of people the item browses, the number of people the item joins the shopping cart, and the number of purchases of the item. For example, the number of product views, the number of products that a product has entered a shopping cart, and the number of product purchases within 30 days of a product.
The plurality of value sets may be represented, for example, in the form of a value matrix. One row in the numerical matrix comprises a plurality of numerical values of a plurality of commodities on a predetermined index, and one column in the numerical matrix comprises a plurality of numerical values of a commodity on a plurality of predetermined indexes.
At block 206, the computing device 110 generates a plurality of entropies associated with a plurality of predetermined metrics based on the plurality of sets of values. The method for generating the plurality of entropies will be described in detail below in conjunction with fig. 5.
At block 208, the computing device 110 generates a plurality of weights associated with a plurality of predetermined metrics based on the plurality of entropies.
In particular, for a certain predetermined metric, its associated weight may be determined based on the entropy associated with the predetermined metric and a plurality of entropies associated with a plurality of predetermined metrics. Taking 3 predetermined indexes as an example, the formula of the weight is as follows.
Wherein
Represents the entropy of the jth predetermined index,
representing the weight of the jth predetermined index.
At block 210, the computing device 110 generates a plurality of interactive behavior values associated with the plurality of items 120-1 through 120-m based on the plurality of weights and the plurality of sets of values.
Specifically, for each of the plurality of items 120-1 through 120-m, an interactive behavior value associated with the item is generated based on the plurality of weights and the plurality of values included in the set of values associated with the item. Taking 3 predetermined indexes as an example, the calculation formula of the interactive behavior value is as follows.
Wherein
The value of the ith product on the jth predetermined index is shown. For example, if the weight of the number of browsing persons of the product is 0.19, the weight of the number of persons of the product who join the shopping cart is 0.31, and the weight of the number of purchased products is 0.5, the value of the interaction behavior of the product is 0.19 +0.31 + 0.5.
At block 212, the computing device 110 generates a plurality of total heat values associated with the plurality of items 120-1 through 120-m based on the plurality of heat values and the plurality of interactive behavior values.
For example, for each of the plurality of items 120-1 through 120-m, the heat value associated with the item and the interactive behavior value associated with the item may be added to generate a total heat value associated with the item.
At block 214, computing device 110 generates, for presentation, first ranking result 140 for the plurality of items 120-1 through 120-m at the current time based on the plurality of total calorific values. In particular, the plurality of items may be sorted by total heat value from high to low, generating a first sorted result for presentation. For example, the computing device 110 may send the first ranking results to a terminal device for presentation.
Therefore, the commodity heat value can be comprehensively calculated in multiple dimensions to serve as a commodity sequencing basis, so that new commodities or potential commodities have more exposure opportunities, and mass commodities can be better seen by users. In addition, the comprehensive heat value of the commodity is calculated by utilizing a time attenuation model and a behavior weighting algorithm, the existing commodity list page sequencing is optimized, the problem of cold start of the new commodity is solved, and the conversion rate and the sales volume of the mall are improved while the user experience is improved.
FIG. 3 shows a flow diagram of a method 300 for generating a plurality of heat value according to an embodiment of the present disclosure. For example, the method 300 may be performed by the computing device 110 as shown in FIG. 1. It should be understood that method 300 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.
At block 302, the computing device 110 determines a plurality of shelf life times for the plurality of items 120-1 through 120-m based on the plurality of shelf times and the current time.
At block 304, the computing device 110 determines a plurality of attenuation factors for the plurality of items 120-1 to 120-m based on the plurality of shelf life durations and the predetermined attenuation coefficient.
For example, the heat of the commodity can be reduced to 0 after 45 days, namely, the heat is approximately equal to
So that the attenuation coefficient k =0.20467 is predetermined, wherein
Is the initial heat value.
For a certain product, the attenuation factor can be determined by the following formula.
Where k is a predetermined attenuation coefficient, t is the current time,
the shelf life is shown.
At block 306, the computing device 110 generates a plurality of heat value values for the plurality of items 120-1 to 120-m at the current time based on the plurality of decay factors and the plurality of initial heat value.
For a certain product, its heat value can be determined by the following formula.
Thereby, the commodity heat is attenuated along with time.
Alternatively or additionally, in some embodiments, the computing device 110 may also perform the following steps for each of the plurality of items: a second ranking result of the set of total heat values for the set of items at the time of the item being shelved is determined. The second ranking result is, for example, a result of ranking the total set of heat values from high to low. Subsequently, the computing device 110 may determine the category to which the item belongs, and determine the number of items of the item within the category. Next, the computing device 110 may determine a total heat value from the second ranking result as an initial heat value for the item based on the number of categories of the item. The number of classes of goods is for example the number of Standard Product Units (SPUs). For example, the items may be divided into 3 large categories, and there may be multiple categories of items within each category.
Fig. 4 shows a flow diagram of a method 400 for determining a total calorific value from a second sorting result according to an embodiment of the present disclosure. For example, the method 400 may be performed by the computing device 110 as shown in FIG. 1. It should be understood that method 400 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.
At block 402, the computing device 110 determines whether the number of categories of items is greater than a first threshold. The first threshold value is, for example, 100.
If, at block 402, the computing device 110 determines that the number of categories of items of the item is greater than the first threshold, then, at block 404, a total heat value at the first predetermined quantile is determined from the second sorted results as an initial heat value for the item. The first predetermined quantile is, for example, the 11 th bit.
If, at block 402, the computing device 110 determines that the number of categories of merchandise is less than or equal to the first threshold, then, at block 406, it is determined whether the number of categories of merchandise is greater than the second threshold. The second threshold value is, for example, 50.
If the computing device 110 determines at block 406 that the number of categories of items of the item is greater than the second threshold, then at block 408 a total heat value at a second predetermined quantile is determined from the second ranking result as the initial heat value for the item, the second predetermined quantile being less than the first predetermined quantile. The second predetermined quantile is, for example, bit 9.
If, at block 406, the computing device 110 determines that the number of categories of merchandise is less than or equal to the second threshold, then, at block 410, it is determined whether the number of categories of merchandise is greater than a third threshold. The third threshold value is, for example, 20.
If the computing device 110 determines that the number of categories of items of the item is greater than the third threshold at block 410, a total heat value at a third predetermined quantile, which is less than the second predetermined quantile, is determined from the second ordering result as the initial heat value for the item at block 412. The third predetermined quantile is, for example, bit 5.
If the computing device 110 determines at block 410 that the number of categories of items of the item is less than or equal to the third threshold value, a total heat value at a fourth predetermined quantile, which is less than the third predetermined quantile, is determined from the second sorted results as the initial heat value for the item at block 414. The fourth predetermined quantile is, for example, the 2 nd bit.
Thus, different initial heat value is given to the commodities of different categories based on the number of the commodity categories in the categories, so that the initial heat value is closer to the actual situation.
Fig. 5 shows a flow diagram of a method 500 for generating a plurality of entropies, according to an embodiment of the disclosure. For example, the method 500 may be performed by the computing device 110 as shown in fig. 1. It should be understood that method 500 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.
At block 502, the computing device 110 generates a plurality of specific gravity sets of the plurality of items 120-1 through 120-m on a plurality of predetermined metrics based on the plurality of value sets. Each of the plurality of specific gravity sets includes a plurality of specific gravities of the associated item at a plurality of predetermined indicators. Each of the plurality of specific gravities represents a ratio of a value of the associated article on the corresponding predetermined index to a sum of a plurality of values of the plurality of articles on the corresponding predetermined index.
The specific gravity of the ith product on the jth predetermined index can be determined by the following formula.
Wherein
The value of the ith product on the jth predetermined index is shown, and m represents the number of the plurality of products.
In some embodiments, prior to determining the plurality of specific gravity sets, normalizing the plurality of numerical value sets is further included. Specifically, for each of a plurality of predetermined criteria, a difference between a maximum value and a minimum value of a plurality of items on the predetermined criteria is determined, and based on the maximum value and the minimum value, a plurality of normalized values of the plurality of items on the predetermined criteria is determined. The specific formula for normalizing the values is shown below.
Wherein
A value representing the ith product on the jth predetermined index,
representing the minimum value of a plurality of items on the jth predetermined index,
the maximum value of the plurality of commodities on the jth preset index is shown.
At block 504, the computing device 110 generates, for each of a plurality of predetermined metrics, an entropy associated with the predetermined metric based on a plurality of specific gravities of the plurality of items 120-1 through 120-m over the predetermined metric.
A specific formula for generating entropy may be as follows.
Wherein,
the specific gravity of the ith product on the jth predetermined index is represented, and m represents the number of the plurality of products.
Therefore, the entropy of the predetermined index can be determined by the specific gravity of the plurality of commodities on the predetermined index, and the weight of the predetermined index can be determined accurately.
Fig. 6 illustrates a schematic block diagram of an example device 600 that can be used to implement embodiments of the present disclosure. For example, computing device 110 as shown in FIG. 1 may be implemented by device 600. As shown, device 600 includes a Central Processing Unit (CPU) 601 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 602 or loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the random access memory 603, various programs and data required for the operation of the device 600 can also be stored. The central processing unit 601, the read only memory 602, and the random access memory 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the input/output interface 605, including: an input unit 606 such as a keyboard, a mouse, a microphone, and the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The various processes and processes described above, such as the method 200 and 500, may be performed by the central processing unit 601. For example, in some embodiments, the method 200-500 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the read only memory 602 and/or the communication unit 609. When the computer program is loaded into the random access memory 603 and executed by the central processing unit 601, one or more of the actions of the method 200 and 500 described above may be performed.
The present disclosure relates to methods, apparatuses, systems, electronic devices, computer-readable storage media and/or computer program products. The computer program product may include computer-readable program instructions for performing various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (8)
1. A method for merchandise sorting, comprising:
generating a plurality of heat values of a plurality of commodities at a current time based on a plurality of shelf-loading times, a plurality of initial heat values and a time decay model of the commodities;
generating a plurality of value sets of the plurality of commodities on a plurality of predetermined indexes based on historical data of the commodities within a preset time interval from the current time, wherein each value set in the value sets comprises a plurality of values of the associated commodity on the predetermined indexes;
generating a plurality of entropies associated with the plurality of predetermined metrics based on the plurality of sets of values;
generating a plurality of weights associated with the plurality of predetermined metrics based on the plurality of entropies;
generating a plurality of interactive behavior values associated with the plurality of items based on the plurality of weights and the plurality of sets of values;
generating a plurality of total heat values associated with the plurality of commodities based on the plurality of heat values and the plurality of interactive behavior values; and
generating, for presentation, a first ranking result of the plurality of items at the current time based on the plurality of total heat values.
2. The method of claim 1, wherein generating the plurality of heat values comprises:
determining a plurality of shelf loading durations for the plurality of commodities based on the plurality of shelf loading times and the current time;
determining a plurality of attenuation factors for the plurality of merchandise based on the plurality of shelf life times and a predetermined attenuation coefficient; and
generating the plurality of heat value for the plurality of items at the current time based on the plurality of decay factors and the plurality of initial heat value.
3. The method of claim 1, further comprising, for each of the plurality of items, performing the steps of:
determining a second ordering result of the set of total heat values of the set of commodities at the time of putting on shelf of the commodities;
determining a category to which the commodity belongs;
determining the number of categories of merchandise within the category; and
determining a total heat value from the second ranking result as an initial heat value for the item based on the item quantity of the item.
4. The method of claim 3, wherein determining the total heat value comprises:
if the number of the commodity categories is determined to be larger than a first threshold value, determining a total heat value located at a first preset quantile from the second sorting result as an initial heat value of the commodity;
if the number of the commodity categories is determined to be larger than a second threshold value and smaller than or equal to the first threshold value, determining a total heat value at a second preset quantile from the second sorting result as an initial heat value of the commodity, wherein the second preset quantile is smaller than the first preset quantile;
if the number of the commodity categories is determined to be larger than a third threshold value and smaller than or equal to the second threshold value, determining a total heat value at a third preset quantile from the second sorting result as an initial heat value of the commodity, wherein the third preset quantile is smaller than the second preset quantile; and
if the number of the commodity categories is determined to be smaller than or equal to the third threshold value, determining a total heat value located at a fourth predetermined quantile from the second sorting result as an initial heat value of the commodity, wherein the fourth predetermined quantile is smaller than the third predetermined quantile.
5. The method of claim 1, wherein generating the plurality of entropies comprises:
generating a plurality of specific gravity sets of the plurality of commodities on the plurality of predetermined indexes based on the plurality of numerical value sets, each specific gravity set of the plurality of specific gravity sets comprising a plurality of specific gravities of the associated commodity on the plurality of predetermined indexes, each specific gravity of the plurality of specific gravity sets representing a ratio of a numerical value of the associated commodity on a corresponding predetermined index to a sum of a plurality of numerical values of the plurality of commodities on the corresponding predetermined index; and
for each of the plurality of predetermined indicators, entropy associated with the predetermined indicator is generated based on a plurality of specific gravities of the plurality of items on the predetermined indicator.
6. The method of claim 1, wherein the plurality of predetermined metrics includes a number of items viewed, a number of items added to a shopping cart, and a number of items purchased.
7. An electronic device, comprising:
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 the method of any one of claims 1-6.
8. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.
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