CN110796520A - Commodity recommendation method and device, computing equipment and medium - Google Patents
Commodity recommendation method and device, computing equipment and medium Download PDFInfo
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Abstract
The present disclosure provides a method of merchandise recommendation. The method comprises the following steps: acquiring a repeated purchased commodity set of a first user; determining a repeated purchase time range of each commodity in the repeated purchase commodity set; determining commodities, the repeated purchasing time range of which does not include the current time, in the repeated purchasing commodity set as commodities needing to be eliminated; acquiring a preferred commodity set of a first user; determining a set of commodities to be recommended according to the preferred commodity set and the commodities to be eliminated; and outputting the set of commodities to be recommended. The disclosure also provides a device for recommending commodities, a computing device and a computer readable storage medium.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a computing device, and a medium for recommending a commodity.
Background
In the current e-commerce platform (such as Tianmao, Ronge shopping, Taobao, Jingdong and the like), a mathematical model is established by collecting purchase history data of a user, and commodities which are not purchased by the user are recommended to the user by utilizing a collaborative filtering algorithm. However, the mainstream collaborative filtering algorithm focuses on analyzing the interests and hobbies of users, that is, by statistical analysis of data, users with the same hobbies may buy the same type of products as a starting point, and the preference degrees of the commodities are predicted and recommended by similar users.
However, this method has some problems, such as that a user purchases a television set of a heart instrument by browsing and comparing the goods for a long time on a certain e-commerce platform, and the user receives many goods recommendations about the television set and related home appliances on the home page of the e-commerce platform in the near term according to the existing recommendation algorithm. However, the large-scale consumer product such as the television is not purchased again within three years with a high probability (abnormal loss, such as accidental damage and the like is eliminated), so that the recommendation method is poor in accuracy and user experience.
Disclosure of Invention
One aspect of the present disclosure provides a method of merchandise recommendation, including: acquiring a repeated purchased commodity set of a first user; determining a repeated purchase time range of each commodity in the repeated purchase commodity set; determining commodities, the repeated purchasing time range of which does not include the current time, in the repeated purchasing commodity set as commodities needing to be eliminated; acquiring a preferred commodity set of a first user; determining a set of commodities to be recommended according to the preferred commodity set and the commodities to be eliminated; and outputting the set of commodities to be recommended.
Optionally, the determining a repeat purchase time range for each item in the repeat purchase item set includes: acquiring the purchase time of repeatedly purchasing each commodity in the commodity set; determining at least one repeat purchase cycle for each commodity based on the purchase time for each commodity; and estimating the repeated purchase time range of each commodity according to at least one repeated purchase period of each commodity.
Optionally, the estimating a repeat purchase time range of each product according to at least one repeat purchase cycle of each product includes: for each commodity in the set of repeatedly purchased commodities, determining the sum of the largest repeated purchase period in the at least one repeated purchase period of the commodity and the latest purchase time of the commodity as the upper bound of the repeated purchase time range, and determining the sum of the smallest repeated purchase period in the at least one repeated purchase period of the commodity and the latest purchase time of the commodity as the lower bound of the repeated purchase time range.
Optionally, in the above determining the repeatedly purchased commodity set, the commodity whose repeatedly purchased time range does not include the current time is taken as a commodity to be excluded, and the determining includes: acquiring current time; judging whether the repeated purchase time range of the commodities contains the current time or not aiming at each commodity in the repeated purchase commodity set; and if the repeated purchase time range of the commodity contains the current time, determining the commodity as the commodity needing to be eliminated.
Optionally, the obtaining the set of repeatedly purchased commodities for the first user includes: acquiring a plurality of original commodities purchased by a first user within a preset time period; determining a repeat purchase cycle category of each of a plurality of original commodities, the repeat purchase cycle category comprising a zero repeat purchase cycle, a long repeat purchase cycle and a short repeat purchase cycle; and forming a repeated purchase commodity set by commodities with repeated purchase cycles of zero repeated purchase cycle and short repeated purchase cycle in the plurality of original commodities.
Optionally, the obtaining the preferred commodity set of the first user includes: determining the similarity of a first user and a plurality of second users; determining a second user with the similarity larger than the similarity threshold value with the first user from the plurality of second users as a third user; and determining a set of preferred goods based on the goods preferred by the first user and the goods preferred by the third user.
Optionally, the determining the set of to-be-recommended commodities according to the set of preference commodities and the commodities to be excluded includes: and removing the commodities needing to be excluded from the preference commodity set to obtain a to-be-recommended commodity set.
Another aspect of the present disclosure provides an apparatus for merchandise recommendation, including: the first acquisition module is used for acquiring a repeated purchased commodity set of a first user; the system comprises a first determining module, a second determining module and a control module, wherein the first determining module is used for determining the repeated purchase time range of each commodity in a repeated purchase commodity set; the second determining module is used for determining that the commodities, of which the repeated purchasing time range does not include the current time, in the repeated purchasing commodity set are taken as the commodities needing to be eliminated; the second acquisition module is used for acquiring the preferred commodity set of the first user; the third determining module is used for determining a to-be-recommended commodity set according to the preference commodity set and the commodities needing to be eliminated; and the output module is used for outputting the set of the commodities to be recommended.
Another aspect of the disclosure provides a computing device comprising: one or more processors; storage means for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described above.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
According to the embodiment of the disclosure, the commodities, the time range of which does not include the current time, of the repeatedly purchased commodity set are determined to be the commodities needing to be removed, and then the commodity set to be recommended is determined according to the preferred commodity set and the commodities needing to be removed, so that the commodities needing to be purchased can be recommended to the user more accurately, and the user experience can be improved.
Drawings
For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
fig. 1 schematically illustrates an application scenario of a method and apparatus for merchandise recommendation according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a method of merchandise recommendation in accordance with an embodiment of the present disclosure;
FIG. 3 schematically illustrates an example flow diagram for obtaining a set of repeat purchases of items by a first user in accordance with an embodiment of the disclosure;
FIG. 4 schematically illustrates an example flow chart for determining a repeat purchase time range for repeatedly purchasing items from a set of items according to an embodiment of this disclosure;
FIG. 5 schematically illustrates an example flow chart for obtaining a preferred merchandise set for a first user according to an embodiment of this disclosure;
FIG. 6 schematically illustrates a block diagram of an apparatus for merchandise recommendation, in accordance with an embodiment of the present disclosure; and
FIG. 7 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon for use by or in connection with an instruction execution system.
The embodiment of the disclosure provides a commodity recommendation method and a commodity recommendation device. The method comprises the steps of obtaining a repeated purchased commodity set of a first user; determining a repeated purchase time range of each commodity in the repeated purchase commodity set; determining commodities, the repeated purchasing time range of which does not include the current time, in the repeated purchasing commodity set as commodities needing to be eliminated; acquiring a preferred commodity set of a first user; determining a set of commodities to be recommended according to the preferred commodity set and the commodities to be eliminated; and outputting the set of commodities to be recommended.
Fig. 1 schematically illustrates an application scenario of the method and apparatus for commodity recommendation according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as shopping applications, web browser applications, social platform software, etc. (by way of example only). The user can access the e-commerce platform through the communication client application installed on the terminal device 101, 102, 103 and make an online purchase in the e-commerce platform.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a back-office management server (for example only) that provides support for e-commerce platforms browsed by users using the terminal devices 101, 102, 103. The background management server may collect, analyze, and count data such as the received user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the method for recommending goods provided by the embodiment of the present disclosure may be executed by the server 105. Accordingly, the device for recommending goods provided by the embodiment of the present disclosure may be disposed in the server 105. The method for recommending goods provided by the embodiment of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the device for recommending goods provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
FIG. 2 schematically shows a flow chart of a method of merchandise recommendation according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S210 to S260.
In operation S210, a set of repeat purchases of a first user is obtained.
According to an embodiment of the present disclosure, operation S210 may include, for example, acquiring historical purchase data of the first user in the e-commerce platform, determining commodities with a purchase frequency more than two times from the historical purchase data, then removing commodities with a low possibility of repeated purchase from the commodities, and grouping the remaining commodities into a repeatedly purchased commodity set.
In operation S220, a repeat purchase time range for repeatedly purchasing each item in the item set is determined.
According to an embodiment of the present disclosure, the time range in which the product is purchased again may be estimated according to the repeated purchase cycle (interval between two adjacent purchase times) of the product in the historical purchase data.
Based on this, operation S220 may include, for example, determining, for each item of the set of repeated purchases of the item, a sum of a largest repeated purchase period of the at least one repeated purchase period of the item and a most recent purchase time of the item as an upper bound of the repeated purchase time range, and a sum of a smallest repeated purchase period of the at least one repeated purchase period of the item and a most recent purchase time of the item as a lower bound of the repeated purchase time range.
In operation S230, it is determined that the goods whose repeat purchase time range does not include the current time in the repeat purchase goods set are the goods that need to be excluded.
According to an embodiment of the present disclosure, operation S230 may include, for example, determining that a repeat purchase time range for each item in the repeat purchase item set is compared with a current time. And if the current time is earlier than the lower limit of the repeated purchase time range or the current time is greater than the upper limit of the repeated purchase time range, taking the commodity as the commodity to be eliminated.
In operation S240, a preferred goods set of the first user is acquired.
According to an embodiment of the present disclosure, operation S240 may include, for example, obtaining goods frequently purchased by the first user (the number of purchases is greater than the threshold number of purchases) by analyzing historical purchase data of the first user, and grouping the goods into a preferred goods set of the user.
In operation S250, a set of goods to be recommended is determined according to the set of preferred goods and the goods to be excluded.
According to an embodiment of the present disclosure, operation S250 may include, for example, removing the item to be excluded from the set of preferred items, and obtaining a set of items to be recommended.
In operation S260, a set of goods to be recommended is output.
According to the embodiment of the disclosure, the output set of commodities to be recommended can be recommended to the first user, so that commodities required by the user can be recommended to the user more accurately, and the user experience is improved.
The method shown in fig. 2 is further described with reference to fig. 3-5 in conjunction with specific embodiments.
Firstly, determining the repeated purchase cycle of each commodity according to all commodity purchase data in an electronic commerce platform, and classifying all commodities into three categories according to the rule of the repeated purchase cycle: zero repeated purchase cycle, long repeated purchase cycle, short repeated purchase cycle, and the corresponding relation between each commodity and each category is stored.
Next, operation S210 is performed to obtain a set of repeatedly purchased goods of the first user.
FIG. 3 schematically shows an example flow diagram for obtaining a set of repeat purchases of items by a first user, in accordance with an embodiment of the disclosure.
As shown in fig. 3, operation S210 may include the following operations S310 to S330.
In operation S310, a plurality of original goods purchased by a first user within a preset time period are acquired.
According to the embodiment of the present disclosure, the commodity (i.e., the original commodity) purchased by the first user within the preset time period may be acquired by acquiring the purchase data of the first user within the preset time period. The preset time period may be a continuous time period or a discontinuous time period. Illustratively, the preset time period in the present embodiment is the last 1 year.
In operation S320, a repeat purchase cycle category of each of a plurality of original goods is determined.
According to embodiments of the present disclosure, the repeat purchase cycle categories may include, for example, a zero repeat purchase cycle, a long repeat purchase cycle, and a short repeat purchase cycle. The commodity with the zero repeated purchasing cycle refers to a commodity with a generally short repeated purchasing cycle, namely, a consumable such as a cotton swab and the like with frequent repeated purchasing. The commodity with a long repeated purchase cycle refers to an article with a repeated purchase cycle with a high probability of three years and more, such as a valuable article, a large household appliance, a diamond ring and the like. The commodity with a short repeated purchase cycle refers to a commodity with a repeated purchase frequency less than zero repeated purchase cycle, and the repeated purchase cycle is within three years, such as daily necessities such as skin care products.
According to the embodiment of the disclosure, the repeated purchase cycle type identifier Bp can be set to represent the repeated purchase cycle type of the commodity, and the value example of Bp is as follows:
in operation S330, the commodities, of which the repeat purchase cycle categories are zero repeat purchase cycles and short repeat purchase cycles, among the plurality of original commodities are grouped into a repeat purchase commodity set.
According to the embodiment of the disclosure, the commodities with the zero repeated purchase cycle and the commodities with the short repeated cycle in the original commodities are gathered to form a repeated purchase commodity set.
Next, operation S220 is performed to determine a repeat purchase time range for repeatedly purchasing each item in the item set.
FIG. 4 schematically illustrates an example flow chart for determining a repeat purchase time range for repeatedly purchasing items from a set of items according to an embodiment of this disclosure.
As shown in fig. 4, operation S220 may include, for example, the following operations S410 to S430.
In operation S410, a purchase time for repeatedly purchasing each item in the set of items is acquired.
According to embodiments of the present disclosure, data may be purchased through a first user's history within a last year in an e-commerce platform. The time for each purchase of each item in the set of repeatedly purchased items is obtained.
At least one repeat purchase cycle for each item is determined according to the purchase time of each item in operation S420.
According to an embodiment of the present disclosure, the repeat purchase cycle of each type of goods of the first user may be calculated according to the following formula:
wherein, PtA repeated purchase cycle for the user to repeatedly purchase the commodities for the tth time; t ist+1Time to purchase the class of merchandise for the t +1 th time of the user; t istTime to purchase the item for the user for the tth time; vtThe unit volume of the commodities purchased when the commodities are purchased for the t time; qtThe purchase amount of the goods when purchasing the goods for the t-th time. According to an embodiment of the present disclosure, PtThe unit of (b) may be, for example, 1 day.
For example, the minimum value Bpw is selected from the repeat purchase cycle calculated aboveminAnd maximum value BpwmaxAnd recorded in the repeat purchase cycle table. Except BpwminAnd BpwmaxBesides, the repeated purchase cycle table may further include: repeat purchase cycle number CidCommodity type mark KidRepeated purchase week of the commodityPeriod category flags Bp and BpwminAnd BpwmaxThe unit of (c).
Table 1 below shows an example of a repeat purchase cycle table.
TABLE 1
| Cid | Kid | Bp | Bpwmin | Bpwmax | Unit of |
| 001 | Facial cleanser | 3 | 60 | 180 | 1 day |
| ... | ... | ... | ... | ... | ... |
For example, to exclude the effects of the e-commerce promotional program, the resulting purchase data for the e-commerce promotional program may be removed from the user historical purchase data. Specifically, for the repeated purchase due to the e-commerce sales promotion activity, the purchased goods are counted in the last repeated purchase, and the number of the subsequent repeated purchases is sequentially reduced by 1. For example, if a user purchases a certain product 4 times, and if the 3 rd purchase of the certain product occurs due to an e-commerce sales promotion event, the 3 rd purchased product is counted in the 2 nd purchased product and the original 4 th purchase is counted as the 3 rd purchase when calculating the repeated purchase cycle of the certain product.
In operation S430, a repeat purchase time range of each item is estimated according to at least one repeat purchase period of each item.
According to an embodiment of the present disclosure, operation S430 may include, for example, calculating a latest repeat purchase cycle time and an earliest repeat purchase cycle time according to the following formulas:
wherein, TnowIs the current time; t isKid，buyIs marked as K for commodity kindidThe time of purchase of the article of (1); qKidThe purchase quantity of the purchased commodities; vKidThe capacity of purchased commodities; bp is a commodity repeated purchase cycle category;the commodity type is marked as K under the unit capacityidThe maximum value of the repeated purchase cycle of the commodity;the commodity type is marked as K under the unit capacityidThe minimum value of the repeated purchase cycle of the commodity;
is marked as K for commodity kindidThe latest repeat purchase cycle time of the good;
is marked as K for commodity kindidThe earliest repeat purchase cycle time of the goods.
That is, the repeat purchase time range of the product is
Then, operation S230 is performed to determine that the product whose repeat purchase time range does not include the current time in the repeat purchase product set is the product to be excluded.
Specifically, the current time T is acquirednow. For each commodity, T is judgednowWhether or not in the repeat purchase time range of the goods
If in TnowIs out of position
And if so, the commodity is the commodity to be eliminated.
According to an embodiment of the present disclosure, a buyback status PBp may be set, setting the buyback status of such merchandise to be in a buyback cycle if the current time is within the repeat purchase time range. If the time is less than the earliest repeated purchasing period, the system is set to be in a consumption period. If the time is longer than the latest repeated purchasing cycle time, the state is set to be in a failure state. PBp may be expressed as:
wherein PBp ═ 0 indicates a buyback cycle, PBp ═ 1 indicates a consumption cycle, and PBp ═ 2 indicates a failure state.
Next, operation S240 may be performed to obtain a preferred goods set of the first user.
FIG. 5 schematically illustrates an example flow chart for obtaining a preferred merchandise set for a first user according to an embodiment of this disclosure.
As shown in fig. 5, operation S240 may include, for example, the following operations S510 to S540.
In operation S510, a similarity between a first user and a plurality of second users is determined.
In operation S520, a second user, whose similarity to the first user is greater than a similarity threshold, is determined from among the plurality of second users as a third user.
In operation S530, preference goods of all users are acquired.
In operation S540, a preferred goods set is determined according to goods preferred by the first user and preferred goods of the third user.
According to the embodiment of the disclosure, the similarity refers to the similarity between the first user and the second user for the commodity preference, and the similarity can be calculated by a Pearson correlation coefficient. Specifically, the scores of the first user and the second user for the commodity are firstly acquired. The score is used for reflecting the preference degree of the user for the commodity, and the higher the score is, the higher the preference degree of the user for the commodity is. In this embodiment, the score is given in an exemplary value range of 1 to 5.
Then, the similarity between the first user and each second user is calculated respectively. Let IcnRepresenting the set of commodities scored by the first user c and the second user n at the same time, the similarity between the first user c and the second user n may be calculated according to the following formula:
where sim (c, n) is the similarity between the first user c and the second user n, and r is IcnNumber of commodities in (1), RciFor the first user pair IcnThe score of the ith good in the second item,for the first user c to IcnAverage score of all goods in (1), RniFor the second user pair IcnThe score of the ith good in the second item,for a second user n to IcnAverage scores of all items in (a).
And then selecting the first K second users from large to small according to the obtained similarity, wherein the first K second users are the third users. Respectively calculating the first user c pair IcnA composite score for each of the items in (a). Specifically, K third users are taken as the nearest neighbor user set N of the first user ccCalculating the first user c pair I according to the following formulacnComposite score P of ith commodityci。
Wherein, sim (c, n)j) Is a first user c and a nearest neighbor user set NcThe jth third user n in (1)jSimilarity of (D), RnjiIs a third user njTo IcnThe score of the ith good in the second item,
is a third user njTo IcnAverage scores of all items in (a).
Get PciAnd (5) the commodities more than or equal to 3 form a recommendation set I, and the types of the commodities in the set I are extracted to form a commodity set I' preferred by the user.
Alternatively, the set of preferred items I 'may be supplemented with items of a type that are not included in the set of preferred items I', but that are purchased by the user.
Next, in operation S250, a set of items to be recommended is determined according to the set of preferred items and the items to be excluded. Specifically, the commodities in the consumption cycle and the failure state can be excluded according to the buyback state of each commodity in the preference commodity set, and the remaining commodities are combined into the to-be-recommended commodity set.
Further, the top N (N is a positive integer and is less than or equal to the total number of the types of goods in the to-be-recommended set) types of goods in the to-be-recommended goods set may be selected to form a recommended item set, and goods are recommended to the user according to the recommended item set.
According to the embodiment of the disclosure, the commodities, the time range of which does not include the current time, of the repeatedly purchased commodity set are determined to be the commodities needing to be removed, and then the commodity set to be recommended is determined according to the preferred commodity set and the commodities needing to be removed, so that the commodities needing to be purchased can be recommended to the user more accurately, and the user experience can be improved.
Fig. 6 schematically shows a block diagram of an apparatus for merchandise recommendation according to an embodiment of the present disclosure.
As shown in fig. 6, the apparatus 600 for recommending goods includes a first obtaining module 610, a first determining module 620, a second determining module 630, a second obtaining module 640, a third determining module 650, and an output module 660. The merchandise recommendation apparatus 600 may perform the method described above with reference to fig. 2 to 5.
Specifically, the first obtaining module 610 is configured to obtain a set of repeatedly purchased commodities of the first user.
The first determining module 620 is configured to determine a repeat purchase time range for each item in the repeat purchase item set.
The second determining module 630 is configured to determine that, in the repeatedly purchased goods set, the repeatedly purchased time range does not include the goods at the current time as the goods to be excluded.
The second obtaining module 640 is configured to obtain a preferred commodity set of the first user.
And a third determining module 650, configured to determine a set of goods to be recommended according to the set of preferred goods and the goods that need to be excluded.
And the output module 660 is used for outputting the set of the commodities to be recommended.
According to the embodiment of the disclosure, the commodities, the time range of which does not include the current time, of the repeatedly purchased commodity set are determined to be the commodities needing to be removed, and then the commodity set to be recommended is determined according to the preferred commodity set and the commodities needing to be removed, so that the commodities needing to be purchased can be recommended to the user more accurately, and the user experience can be improved.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any plurality of the first obtaining module 610, the first determining module 620, the second determining module 630, the second obtaining module 640, the third determining module 650, and the outputting module 660 may be combined in one module to be implemented, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first obtaining module 610, the first determining module 620, the second determining module 630, the second obtaining module 640, the third determining module 650, and the output module 660 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware by any other reasonable manner of integrating or packaging a circuit, or implemented in any one of three implementations of software, hardware, and firmware, or in a suitable combination of any of them. Alternatively, at least one of the first obtaining module 610, the first determining module 620, the second determining module 630, the second obtaining module 640, the third determining module 650, and the output module 660 may be at least partially implemented as a computer program module, which when executed, may perform a corresponding function.
FIG. 7 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 7 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
As shown in fig. 7, computer system 700 includes a processor 710 and a computer-readable storage medium 720. The computer system 700 may perform a method according to an embodiment of the disclosure.
In particular, processor 710 may comprise, for example, a general purpose microprocessor, an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), and/or the like. The processor 710 may also include on-board memory for caching purposes. Processor 710 may be a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
Computer-readable storage medium 720, for example, may be a non-volatile computer-readable storage medium, specific examples including, but not limited to: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and so on.
The computer-readable storage medium 720 may include a computer program 721, which computer program 721 may include code/computer-executable instructions that, when executed by the processor 710, cause the processor 710 to perform a method according to an embodiment of the disclosure, or any variation thereof.
The computer program 721 may be configured with, for example, computer program code comprising computer program modules. For example, in an example embodiment, code in computer program 721 may include one or more program modules, including 721A, modules 721B, … …, for example. It should be noted that the division and number of modules are not fixed, and those skilled in the art may use suitable program modules or program module combinations according to actual situations, so that the processor 710 may execute the method according to the embodiment of the present disclosure or any variation thereof when the program modules are executed by the processor 710.
According to an embodiment of the present invention, at least one of the first obtaining module 610, the first determining module 620, the second determining module 630, the second obtaining module 640, the third determining module 650, and the output module 660 may be implemented as a computer program module described with reference to fig. 7, which, when executed by the processor 710, may implement the respective operations described above.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: 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 portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
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 code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, 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 or flowchart illustration, and combinations of blocks in the block diagrams 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.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
While the disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (10)
1. A method of merchandise recommendation, comprising:
acquiring a repeated purchased commodity set of a first user;
determining a repeat purchase time range of each commodity in the repeat purchase commodity set;
determining commodities, the repeated purchasing time range of which does not include the current time, in the repeated purchasing commodity set as commodities needing to be excluded;
acquiring a preferred commodity set of the first user;
determining a set of commodities to be recommended according to the preference commodity set and the commodities needing to be excluded; and
and outputting the set of the commodities to be recommended.
2. The method of claim 1, wherein said determining a repeat purchase time range for each item in said set of repeat purchases includes:
acquiring the purchase time of each commodity in the repeated purchase commodity set;
determining at least one repeated purchase cycle for each commodity according to the purchase time of each commodity; and
and estimating the repeated purchase time range of each commodity according to the at least one repeated purchase period of each commodity.
3. The method of claim 2, wherein said estimating a repeat purchase time range for each item based on said at least one repeat purchase cycle for each item comprises:
for each item in the set of repeat purchases of items, determining a sum of a largest repeat purchase period of the at least one repeat purchase period of the item and a most recent purchase time of the item as an upper bound of the repeat purchase time range, and determining a sum of a smallest repeat purchase period of the at least one repeat purchase period of the item and a most recent purchase time of the item as a lower bound of the repeat purchase time range.
4. The method of claim 1, wherein the determining that the item in the set of repeatedly purchased items, whose repeatedly purchased time range does not include the item at the current time, is to be excluded comprises:
acquiring current time;
for each commodity in the repeated purchase commodity set, judging whether the current time is included in the repeated purchase time range of the commodity; and
and if the repeated purchasing time range of the commodity contains the current time, determining the commodity as the commodity needing to be eliminated.
5. The method of claim 1, wherein said obtaining a set of repeat purchases of the first user comprises:
acquiring a plurality of original commodities purchased by the first user within a preset time period;
determining a repeat purchase cycle category for each of the plurality of original goods, the repeat purchase cycle category comprising a zero repeat purchase cycle, a long repeat purchase cycle, and a short repeat purchase cycle; and
and forming the repeated purchase commodity set by the commodities with repeated purchase cycles of zero repeated purchase cycle and short repeated purchase cycle in the plurality of original commodities.
6. The method of claim 1, wherein the obtaining the set of preferred items of the first user comprises:
determining similarity of the first user and a plurality of second users;
determining a second user with the similarity larger than a similarity threshold value with the first user from the plurality of second users as a third user; and
and determining the preferred commodity set according to the commodities preferred by the first user and the preferred commodities of the third user.
7. The method of claim 1, wherein the determining a set of items to be recommended according to the set of preferred items and the items to be excluded comprises:
and removing the commodities needing to be eliminated from the preference commodity set to obtain a to-be-recommended commodity set.
8. An apparatus for merchandise recommendation, comprising:
the first acquisition module is used for acquiring a repeated purchased commodity set of a first user;
the first determining module is used for determining the repeated purchase time range of each commodity in the repeated purchase commodity set;
the second determining module is used for determining that the commodities, the repeated purchasing time range of which does not include the current time, in the repeated purchasing commodity set are taken as the commodities needing to be eliminated;
the second acquisition module is used for acquiring the preferred commodity set of the first user;
the third determining module is used for determining a to-be-recommended commodity set according to the preference commodity set and the commodities needing to be eliminated; and
and the output module is used for outputting the set of the commodities to be recommended.
9. A computing device, comprising:
one or more processors;
a memory for storing one or more computer programs,
wherein the one or more computer programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1 to 7.
10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 7.
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