Method and apparatus for generating object combination, electronic device, and readable medium

Abstract

The present application discloses a method and apparatus for generating an object combination, an electronic device, and a readable medium. The method comprises: on the basis of category information of each object, determining the category of each object; and on the basis of a category association relationship library and the category of each object, generating at least one object combination, wherein each category association relationship in the category association relationship library comprises at least two categories that meet a combination condition and are determined on the basis of historical interaction result information, the interaction result information is used for reflecting historical interaction between at least two categories, and the categories of at least two objects included in each object combination among the at least one object combination match a category association relationship in the category association relationship library.

Description

Method, device, electronic device and readable medium for generating object combination

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on September 27, 2023, with application number 202311262662.1 and invention name “A method, device, electronic device and readable medium for generating object combinations”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present application relates to the field of computer technology, and in particular to a method, device, electronic device, and readable medium for generating an object combination. Background Art

[0004] With the development of Internet technology, people can operate online objects through online behaviors, and staff can also better display objects to people through editing, management, etc. For example, online store staff can display the products and / or services in the online store through editing, management, etc., and users can browse, collect, or purchase the products and / or services in the online store.

[0005] Currently, staff can manually combine multiple objects on the line into object combinations on an online management platform (hereinafter referred to as the management platform), and display the object combinations to people, who can browse, view, and even purchase the object combinations online.

[0006] Summary of the Invention

[0007] In order to solve the above technical problems, the present application provides a method, device, electronic device and readable medium for generating object combinations, which can automatically and intelligently generate object combinations, which not only facilitates the staff, but also ensures the quality of the automatically generated object combinations, thereby making it possible to optimize online marketing situations.

[0008] In order to achieve the above objectives, the technical solutions provided by this application are as follows:

[0009] In a first aspect, the present application provides a method for generating an object combination, comprising:

[0010] Determine the category to which each object belongs based on the category information of each object;

[0011] At least one object combination is generated based on a category association relationship library and the categories to which each object belongs. Each category association relationship in the category association relationship library includes at least two categories that meet the combination conditions based on historical interaction result information. The interaction result information is used to reflect the historical interactions between the at least two categories. The categories to which the at least two objects included in each object combination in the at least one object combination belong match a category association relationship in the category association relationship library.

[0012] In a possible implementation, the category association relationship library includes a first category association relationship, the first category association relationship includes a first category and a second category, and generating at least one object combination according to the category association relationship library and the category to which each object belongs includes:

[0013] Determine a first object set consisting of objects belonging to the first category among the objects;

[0014] determining a second object set consisting of objects belonging to the second category among the objects;

[0015] The at least one object combination is generated according to the first object set and the second object set, and each object combination in the at least one object combination includes any one object in the first object set and any one object in the second object set.

[0016] In a possible implementation, the process of constructing the category association relationship library includes:

[0017] Based on interaction behaviors between objects in a third category and categories of other objects, interaction result information corresponding to the third category is obtained, the interaction result information corresponding to the third category including the number of positive interactions and the ratio of negative interactions between the third category and the other categories, the positive interaction referring to an interaction time between the same user and an object belonging to the third category occurring at a preset time before the user's interaction time with an object belonging to the other category, and the interaction corresponding to the object belonging to the third category and the interaction corresponding to the object belonging to the other category are two adjacent interaction behaviors, the negative interaction ratio referring to the number of interaction behaviors corresponding to the object belonging to the third category that occurred before the interaction corresponding to the object belonging to the other category, and the ratio of the number of interaction behaviors corresponding to the object belonging to the third category, and the interval between the interaction time with the object belonging to the other category and the interaction time with the object belonging to the third category is less than or equal to the preset time;

[0018] determining, based on the number of positive interactions and the reverse interaction ratio, a fourth category that meets the combination condition with the third category, wherein the fourth category belongs to at least one of the other categories;

[0019] The third category and each of the fourth categories are recorded as a category association relationship in the category association relationship library.

[0020] As an example, determining, based on the number of positive interactions and the reverse interaction ratio, a fourth category that meets the combination condition with the third category includes:

[0021] determining, according to the number of positive interactions and the reverse interaction ratio, an association weight between the third category and each of the other categories;

[0022] According to the association weights of the other categories, the fourth category that meets the combination condition is determined, and the combination condition includes: a preset number of the other categories with the largest association weights, or the other categories with association weights greater than a preset weight threshold are determined as the fourth category.

[0023] As another example, determining the fourth category that meets the combination condition based on the association weights of the other categories includes:

[0024] If there is a fifth category in the other categories whose proportion is greater than or equal to the preset proportion threshold, the fourth category that meets the combination condition is determined from the fifth category.

[0025] In a possible implementation manner, objects in the at least one object combination all belong to the same entity.

[0026] In a possible implementation, the method further includes:

[0027] Based on the correlation degree of each object combination in the at least one object combination, a collocation score for each object combination in the at least one object is determined, where the correlation degree of the object combination represents the proportion or number of times that objects belonging to the object combination in the target entity are marketed in combination, and the collocation score is used to guide the display and / or publication of the corresponding object combination.

[0028] In a second aspect, the present application further provides a device for generating an object combination, comprising:

[0029] a determination unit, configured to determine the category to which each object belongs based on the category information of each object;

[0030] A generation unit is configured to generate at least one object combination based on a category association relationship library and the categories to which each object belongs, wherein each category association relationship in the category association relationship library includes at least two categories that meet a combination condition determined based on historical interaction result information, the interaction result information is used to reflect the historical interaction between the at least two categories, and the categories to which the at least two objects included in each object combination in the at least one object combination belong match a category association relationship in the category association relationship library.

[0031] In a possible implementation, the category association relationship library includes a first category association relationship, the first category association relationship includes a first category and a second category, and the generating unit includes:

[0032] A first determining subunit is configured to determine a first object set consisting of objects belonging to the first category among the objects;

[0033] a second determining subunit, configured to determine a second object set consisting of objects belonging to the second category among the objects;

[0034] A generating subunit is used to generate the at least one object combination according to the first object set and the second object set, each object combination in the at least one object combination includes any one object in the first object set and any one object in the second object set.

[0035] In a possible implementation, the device further includes: a construction unit;

[0036] The construction unit is used to construct a category association relationship library.

[0037] As an example, the building block includes:

[0038] a first obtaining subunit, configured to obtain interaction result information corresponding to the third category based on interaction behaviors between objects of the third category and categories of other objects, the interaction result information corresponding to the third category including the number of positive interactions and the ratio of negative interactions between the third category and the other categories, the positive interaction referring to an interaction time between the same user and an object belonging to the third category occurring at a preset time before the user's interaction time with an object belonging to the other category, and the interaction corresponding to the object belonging to the third category and the interaction corresponding to the object belonging to the other category being two adjacent interaction behaviors, the negative interaction ratio referring to the number of interaction behaviors corresponding to the object belonging to the third category that occurred before the interaction corresponding to the object belonging to the other category, and the ratio of the number of interaction behaviors corresponding to the object belonging to the third category, and the interval between the interaction time with the object belonging to the other category and the interaction time with the object belonging to the third category being less than or equal to the preset time;

[0039] a third determining subunit, configured to determine, based on the number of positive interactions and the reverse interaction ratio, a fourth category that meets the combination condition with the third category, wherein the fourth category belongs to at least one of the other categories;

[0040] The saving subunit is used to record the third category and each of the fourth categories as a category association relationship in the category association relationship library.

[0041] As an example, the third determining subunit includes:

[0042] a first determining module, configured to determine an association weight between the third category and each of the other categories based on the number of positive interactions and the reverse interaction ratio;

[0043] The second determination module is used to determine the fourth category that meets the combination condition based on the association weights of the other categories, and the combination condition includes: a preset number of the other categories with the largest association weights, or determining the other categories with association weights greater than a preset weight threshold as the fourth category.

[0044] As another example, the second determining module is specifically configured to:

[0045] If there is a fifth category in the other categories whose proportion is greater than or equal to the preset proportion threshold, the fourth category that meets the combination condition is determined from the fifth category.

[0046] In a possible implementation manner, objects in the at least one object combination all belong to the same entity.

[0047] In a possible implementation, the apparatus further includes:

[0048] A scoring unit is configured to determine a collocation score for each object combination in the at least one object combination based on the correlation degree of each object combination in the at least one object combination, wherein the correlation degree of the object combination represents the proportion or number of times that objects belonging to the object combination in the target entity are marketed in combination, and the collocation score is used to guide the display and / or publication of the corresponding object combination.

[0049] It should be noted that the specific implementation method of the device and the technical effects achieved can be found in the relevant description of the method provided in the first aspect or any implementation method of the first aspect.

[0050] In a third aspect, the present application further provides an electronic device, comprising: a processor and a memory;

[0051] The memory is used to store instructions or programs;

[0052] The processor is used to execute the instructions or programs in the memory so that the electronic device executes the method provided by the first aspect or any one of the implementations of the first aspect.

[0053] In a fourth aspect, the present application also provides a readable medium, which stores instructions or programs. When the instructions or programs are run on a processor, the processor executes the method provided in the first aspect or any one of the implementations of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] FIG1 is a flow chart of a method for generating an object combination provided in an embodiment of the present application;

[0056] FIG2 is a schematic diagram of a process for constructing a category association relationship library provided in an embodiment of the present application;

[0057] FIG3 is a schematic structural diagram of an apparatus 300 for generating an object combination provided in an embodiment of the present application;

[0058] FIG4 is a schematic structural diagram of an electronic device 400 provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] In some cases, users want to view and purchase multiple objects with specific relationships through online entities. To facilitate this, online entities often display multiple objects adjacent to each other or as a group. Displaying multiple objects as a group can be understood as combining multiple objects. For example, an online store might combine nursing pads, postpartum caps, and postpartum shoes into a maternity bag. Another example is combining shirts and trousers into a commuter suit.

[0060] Currently, staff can manually combine multiple online objects into object combinations on an online management platform (hereinafter referred to as the management platform) and display the object combinations to users, who can then browse, view, and even purchase the object combinations online. However, this manual object combination method is inconvenient for staff, and the resulting object combinations vary greatly. Consequently, optimizing online marketing through object combination cannot be achieved.

[0061] Online entity staff usually manually enter the matched object combinations on the online entity's operating platform based on their own experience or sales data of each object. For example, the online store's operating platform has a "product matching" function. The staff enters the editing page through "New Matching" and creates a matching combination on the editing page by uploading the matching main picture, adding products, etc. After the matching combination passes the review, it can be published on the shelf for users to view and / or purchase. Although this process can facilitate users to view and / or purchase on the online entity, it is not very user-friendly for the online entity's staff. On the one hand, the staff needs to select the objects they want to match and perform complex editing operations. On the other hand, the quality of the object combinations generated by the staff's manual object matching cannot be guaranteed. In many cases, the object combinations provided cannot meet the needs of users. Therefore, the current method of matching objects is time-consuming and labor-intensive and has little effect.

[0062] Based on this, an embodiment of the present application provides a method for generating object combinations. Taking the management platform of an online entity as an example, an automatic matching function is integrated in the background of the management platform, so that the management platform can provide the automatic matching function for the staff of the online entity. Taking the automatic matching needs of the staff of the online entity as an example, the device for generating object combinations can first determine the category to which each object belongs based on the category information of each object; then, based on the category association relationship library and the category to which each object belongs, generate at least one object combination. Each category association relationship in the category association relationship library includes at least two categories that meet the combination conditions based on historical interaction result information. The interaction result information is used to reflect the historical interaction between the at least two categories. The categories to which the at least two objects included in each object combination in at least one object combination belong match a category association relationship in the category association relationship library.

[0063] In this way, there is no need for staff to manually match objects. Due to the automatic matching function integrated in the background of the management platform, the staff only needs to use the automatic matching function. The management platform can build and maintain the category association library with the help of the historical interaction result information reflecting the historical interactions between at least two categories, and automatically determine the object combination according to the matching results between the category to which each object belongs and the category association library. On the one hand, the purpose of automatically generating object combinations is achieved. On the other hand, since the category association library is determined by historical interaction result information, using the category association library as a matching benchmark is a reliable data basis, which makes the quality of the generated object combination better, thereby making it possible to optimize online marketing.

[0064] It should be noted that the management platform of an online entity can be: a management platform provided by the e-commerce enterprise hosting the online entity, which can be operated by the online entity after logging in. The management platform can be in the form of a client or a web page.

[0065] It should be noted that the subject implementing the method of generating an object combination may be the device for generating an object combination provided in the embodiment of the present application. The device for generating an object combination may be carried in an electronic device or a functional module of an electronic device. Taking the scenario of the management platform of an online entity as an example, the device for generating an object combination may include the management platform of an online entity, or the device for generating an object combination may be connected to the management platform of an online entity; the electronic device may be installed with a client corresponding to the management platform of the online entity, or may access the webpage corresponding to the management platform of the online entity.

[0066] FIG1 is a flow chart of a method for generating an object combination provided in an embodiment of the present application. The method can be applied to an apparatus for generating an object combination, such as apparatus 300 for generating an object combination shown in FIG3 below. In the scenario of an online entity management platform, the apparatus for generating an object combination can include the online entity management platform or be connected to the online entity management platform. Therefore, the method can also be considered to be applied to the online entity management platform.

[0067] As shown in FIG1 , the method may include, for example, the following S101 to S102:

[0068] S101: Determine the category to which each object belongs based on the category information of each object.

[0069] It is understood that this method can automatically match objects within a certain entity. In this case, each object in S101 can refer to all or part of the objects within the target entity. If each object in S101 refers to a part of the objects within the target entity, then the method can further include: selecting objects to be automatically matched from all objects within the target entity. The selection can be based on one or more pieces of historical information about the objects, such as sales volume, number of views, number of favorites, etc.

[0070] In an embodiment of the present application, the online entity's management platform includes an operation item corresponding to the automatic matching function. By performing an action on the operation item, a staff member is deemed to have performed the target operation on the management platform. The execution of the target operation triggers the management platform's backend to automatically match objects for the staff member's corresponding target entity. For example, the interface corresponding to the online entity's management platform may include a "Smart Matching Recommendation" button. The "Smart Matching Recommendation" button corresponds to the operation item corresponding to the automatic matching function on the online entity's management platform. When a user clicks the "Smart Matching Recommendation" button, it is equivalent to performing the target operation on the management platform, thereby triggering the management platform to execute the method.

[0071] When the backend of the management platform automatically matches objects for the target entity, it is necessary to first obtain the category information of each object in the target entity as the data basis for matching. Category information can refer to information that can indicate the category to which the object belongs. For example, the category information can be the name of the category or the number of the category. For example, for the object lipstick, the category information can be "makeup", so that it can be determined that lipstick belongs to the category of "makeup"; for another example, the category information of the object lipstick can also be "001", and "001" is the category number of "makeup", so that it can be determined that lipstick belongs to the category of "makeup".

[0072] S102. Generate at least one object combination based on a category association relationship library and the categories to which each object belongs. Each category association relationship in the category association relationship library includes at least two categories that meet a combination condition determined based on historical interaction result information. The interaction result information is used to reflect the historical interaction between the at least two categories. The categories to which the at least two objects included in each object combination in the at least one object combination belong match a category association relationship in the category association relationship library.

[0073] In a possible implementation, S102 may include, for example, inputting the categories of the objects included in the target entity into a collocation algorithm, and the collocation algorithm outputting at least one object combination of the target entity. The collocation algorithm may include a category association relationship library.

[0074] Taking online entities as an example, the objects involved in at least one object combination belong to the same online entity (i.e., the target entity). Among them, the online entity can refer to a store in an e-commerce enterprise, the object can correspond to the goods and / or services in the store, and the object combination is a combination of at least two goods and / or services in the store. For example, the target entity corresponds to the B flagship store on the A e-commerce enterprise, the object refers to the goods a1, a2, ..., a100 in the B flagship store, and the object combination can be a combination formed by the combination of at least two objects in a1, a2, ..., a100, such as a1+a3+a100, a2+a30, and a5+a33+a100, which can all be regarded as an object combination of the target entity B.

[0075] Among them, the category association relationship library can be a database including multiple category association relationships that is pre-built and maintained based on historical interaction result information and combination conditions before executing S102. Each category association relationship includes at least two categories. The category association relationship library is used to indicate at least two categories with matching associations to determine the categories that can be combined in the target entity, thereby determining the possible object combinations in the target entity. According to the analysis of historical interaction result information, there is a strong correlation between multiple categories in the same category association relationship, and the possibility of interaction is relatively high. For example, shirts, trousers and leather shoes can be included in a category association relationship, and the correlation is reflected as a professional suit; for example, T-shirts and skirts can be included in another category association relationship, and the correlation is reflected as a common outfit; however, vests and cotton shoes may not be maintained in a category association relationship.

[0076] The historical interaction result information is determined based on statistical analysis of each user's historical interaction behavior data, and reflects the historical interactions between at least two categories. Historical interactions may include the frequency and accuracy of historical interactions. For example, the process of determining the interaction result information may include: for each user, statistically analyzing the historical interactions with objects belonging to different categories to obtain the user's interaction behavior sub-results; and determining the interaction result information based on the interaction behavior sub-results of each user.

[0077] As an example, taking the case where the category association relationship library includes a first category association relationship, and the first category association relationship includes a first category and a second category, S102 may include: determining a first object set consisting of objects belonging to the first category among each object; determining a second object set consisting of objects belonging to the second category among each object; generating the at least one object combination based on the first object set and the second object set, each object combination in the at least one object combination includes any one object in the first object set and any one object in the second object set. For example, the objects belonging to the first category in the target entity include object A, object B and object C, and the objects belonging to the second category in the target entity include object D, object E, object F and object G. Then, the generated object combinations may include: object combination 1 {object A, object D}, object combination 2 {object A, object E}, object combination 3 {object A, object F}, object combination 4 {object A, object G}, object combination 5 {object B, object D}, object combination 6 {object B, object E}, object combination 7 {object B, object F}, object combination 8 {object B, object G}, object combination 9 {object C, object D}, object combination 10 {object C, object E}, object combination 11 {object C, object F} and object combination 12 {object C, object G}.

[0078] As another example, taking the example that the category association relationship library includes a second category association relationship, and the second category association relationship includes a first category, a second category and a third category, S102 may, for example, include: determining a first object set consisting of objects belonging to the first category in each object; determining a second object set consisting of objects belonging to the second category in each object; determining a third object set consisting of objects belonging to the third category in each object; generating the at least one object combination based on at least two object sets among the first object set, the second object set and the third object set, each object combination in the at least one object combination includes any one object corresponding to each of the at least two object sets participating in the combination. For example, the objects belonging to the first category in the target entity include object A and object B, the objects belonging to the second category in the target entity include object C, and the objects belonging to the third category in the target entity include object D and object E. Then, the generated object combinations may include: object combination 1 {object A, object C}, object combination 2 {object B, object C}, object combination 3 {object A, object D}, object combination 4 {object A, object E}, object combination 5 {object B, object D}, object combination 6 {object B, object E}, object combination 7 {object C, object D}, object combination 8 {object C, object E}, object combination 9 {object A, object C, object D}, object combination 10 {object A, object C, object E}, object combination 11 {object B, object C, object D} and object combination 12 {object B, object C, object E}.

[0079] In some implementations, during the construction of the category association relationship library, it is necessary to use the full order database of the online platform as the data source and to collect statistics on orders placed within a preset period of time in the past. The longer the preset period, the richer the data basis for building the category association relationship library, the more meaningful the analyzed interaction result information will be, and the more reliable the resulting category association relationship library will be. Therefore, as an example, statistics can be collected for orders placed at all times in the past. In addition, any order can be included in the statistics. In order to make the statistical results more accurate, normal payment orders that have not been canceled or have not applied for after-sales service can also be filtered out from all orders.

[0080] The process of constructing the category association relationship library can be seen in Figure 2, including: S11, according to the interaction behavior between the objects of the third category and the categories of other objects, obtaining the interaction result information corresponding to the third category, the interaction result information corresponding to the third category includes the number of positive interactions and the reverse interaction ratio between the third category and the other categories, the positive interaction refers to the time when the same user interacts with the objects belonging to the third category at a preset time before the time when the user interacts with the objects belonging to the other categories, and the interaction corresponding to the objects belonging to the third category and the interaction corresponding to the objects belonging to the other categories are two adjacent interaction behaviors, and the reverse interaction ratio refers to the number of positive interactions between the third category and the objects belonging to the other categories. The previous interactive behavior of the interaction corresponding to the target object is the number of interactive behaviors corresponding to the object belonging to the third category, which accounts for the proportion of the number of interactive behaviors corresponding to the object belonging to the third category, and the interval time between the interaction time corresponding to the object belonging to the other category and the interaction time corresponding to the object belonging to the third category is less than or equal to the preset time; S12, based on the number of positive interactions and the reverse interaction ratio, determine the fourth category that meets the combination condition with the third category, and the fourth category belongs to at least one category among the other categories; S13, record the third category and each of the fourth categories as a category association relationship in the category association relationship library. Among them, S12 may include, for example: S121, based on the number of positive interactions and the reverse interaction ratio, determine the association weight of the third category with each of the other categories; S122, based on the association weights of the other categories, determine the fourth category that meets the combination condition. The combination condition may include, for example, a preset number of the other categories with the largest association weights, or determining the other categories with association weights greater than a preset weight threshold as the fourth category. As an example, S122 may include, for example, if there is a fifth category among the other categories whose proportion is greater than or equal to a preset proportion threshold, determining the fourth category that meets the combination condition from the fifth category.

[0081] The size of the association weight can reflect the rationality of the combination of the category.

[0082] For example, taking commodities as an example, if the same user buys commodities in Category B within a preset time (such as 24 hours) after purchasing commodities in Category A, and the user does not purchase other commodities between the purchases of commodities in Category A and Category B (that is, the purchases of commodities in Category A and Category B are two adjacent purchases), it is considered that there is an AB combination. Then, on the one hand, in order to be able to analyze the frequency of matching categories, the number of positive interactions of AB category matching can be counted based on all the purchase records of all users during the entire statistical time (that is, the number of times the same user bought category B products within a preset time after purchasing category A products, and the user did not purchase other products between purchasing category A products and category B products); on the other hand, in order to make the obtained category association relationship more accurate, it is necessary to exclude products that are purchased by universal matching. Therefore, the reverse interaction ratio of AB category matching can also be counted based on all the purchase records of all users during the entire statistical time, that is, the number of times category B products were purchased is 100 times, and the number of times category A products were purchased within a preset time before purchasing category B products and no other products were purchased between purchasing category A products and category B products is 50 times, then the reverse interaction ratio of AB category matching is determined to be 50%. In this way, the product of the number of positive interactions and the reverse interaction ratio can be recorded as the association weight of AB category matching. Based on the above method, the association weights of AB, AC, ..., AZ category matching can be determined. Next, we can first determine whether the reverse interaction ratio of each category combination is greater than a preset threshold (such as 1%). If so, select a preset number (such as 20) category combinations with the largest association weights from the category combinations with a reverse interaction ratio greater than the preset threshold, and record the selected category combinations as a category association relationship, and maintain them in the category association relationship library; if the reverse interaction ratio of each category combination is not greater than the preset threshold, then select a preset number of category combinations with the largest association weights from all category combinations, and record the selected category combinations as a category association relationship, and maintain them in the category association relationship library.

[0083] It should be noted that the category association relationships in the category association relationship library can be updated regularly or irregularly so as to provide accurate data basis for the process of automatically generating object combinations at any time.

[0084] At least one object combination generated based on the category association relationship library can be maintained in a table, which can at least include: the identifier (ID) of category A, the ID of category B and the association weight of "AB category combination". For easy viewing, the table can also include the name of category A and / or the name of category B.

[0085] It is understandable that after generating at least one object combination of the target entity, the at least one object combination of the target entity can be saved in the form of a file for viewing, editing and other operations by staff of the target entity.

[0086] In some possible implementations, in order to subsequently optimize the automatic matching process and to provide a better experience for online entities, the method may further include: calculating the correlation of the object combination based on the number of views, sales, buyer reviews, staff reviews, and sales correlation of each object in the object combination within the target entity, thereby determining the matching score of the object combination based on the correlation. Optionally, the matching score can be displayed on the management platform to guide the display and / or release of the object combination. For example, the staff can choose to put the object combination with a higher matching score on the shelf. For another example, the staff can choose to "adjust" the display order of each object combination on the display page of the online entity according to the size of the matching score. The staff can even set a matching score threshold for the display. Object combinations corresponding to matching scores below the matching score threshold will not be displayed on the display page. For another example, the staff can also influence the matching score of the object combination by filling in a review.

[0087] It should be noted that, for the at least one object combination generated by S102, it can be an object combination that has been deduplicated by the same combination and / or filtered by the same category in the matching; or, at least one object combination generated by S102 can be an object combination that has not been deduplicated by the same combination and / or filtered by the same category in the matching. Then, after S102, the method can also perform deduplication by the same combination and / or filtering by the same category in the matching, so that the object combination finally provided to the staff of the target entity is of better quality, so as to improve the user experience of using the automatic matching function.

[0088] It can be seen that in this method, there is no need for staff to manually match objects. Due to the automatic matching function integrated in the background of the management platform, the staff only needs to use the automatic matching function, and the management platform can build and maintain the category association library with the help of the historical interaction results information reflecting the historical interactions between at least two categories, and automatically determine the object combination according to the matching results between the category to which each object belongs and the category association library. On the one hand, the purpose of automatically generating object combinations is achieved. On the other hand, since the category association library is determined by the historical interaction results information, the category association library is used as the matching benchmark, which is a reliable data basis, making the quality of the generated object combination better, thereby making it possible to optimize online marketing.

[0089] Accordingly, the embodiment of the present application further provides a device 300 for generating an object combination. As shown in FIG3 , the device 300 may include:

[0090] A determination unit 301 is configured to determine the category to which each object belongs based on the category information of each object;

[0091] A generation unit 302 is used to generate at least one object combination based on a category association relationship library and the categories to which each object belongs. Each category association relationship in the category association relationship library includes at least two categories that meet the combination conditions based on historical interaction result information. The interaction result information is used to reflect the historical interaction between the at least two categories. The categories to which the at least two objects included in each object combination in the at least one object combination belong match a category association relationship in the category association relationship library.

[0092] In a possible implementation, the category association relationship library includes a first category association relationship, the first category association relationship includes a first category and a second category, and the generating unit 302 includes:

[0093] A first determining subunit is configured to determine a first object set consisting of objects belonging to the first category among the objects;

[0094] a second determining subunit, configured to determine a second object set consisting of objects belonging to the second category among the objects;

[0095] A generating subunit is used to generate the at least one object combination according to the first object set and the second object set, each object combination in the at least one object combination includes any one object in the first object set and any one object in the second object set.

[0096] In a possible implementation, the apparatus 300 further includes: a construction unit;

[0097] The construction unit is used to construct a category association relationship library.

[0098] As an example, the building block includes:

[0099] a first obtaining subunit, configured to obtain interaction result information corresponding to the third category based on interaction behaviors between objects of the third category and categories of other objects, the interaction result information corresponding to the third category including the number of positive interactions and the ratio of negative interactions between the third category and the other categories, the positive interaction referring to an interaction time between the same user and an object belonging to the third category occurring at a preset time before the user's interaction time with an object belonging to the other category, and the interaction corresponding to the object belonging to the third category and the interaction corresponding to the object belonging to the other category being two adjacent interaction behaviors, the negative interaction ratio referring to the number of interaction behaviors corresponding to the object belonging to the third category that occurred before the interaction corresponding to the object belonging to the other category, and the ratio of the number of interaction behaviors corresponding to the object belonging to the third category, and the interval between the interaction time with the object belonging to the other category and the interaction time with the object belonging to the third category being less than or equal to the preset time;

[0100] a third determining subunit, configured to determine, based on the number of positive interactions and the reverse interaction ratio, a fourth category that meets the combination condition with the third category, wherein the fourth category belongs to at least one of the other categories;

[0101] The saving subunit is used to record the third category and each of the fourth categories as a category association relationship in the category association relationship library.

[0102] As an example, the third determining subunit includes:

[0103] a first determining module, configured to determine an association weight between the third category and each of the other categories based on the number of positive interactions and the reverse interaction ratio;

[0104] The second determination module is used to determine the fourth category that meets the combination condition based on the association weights of the other categories, and the combination condition includes: a preset number of the other categories with the largest association weights, or determining the other categories with association weights greater than a preset weight threshold as the fourth category.

[0105] As another example, the second determining module is specifically configured to:

[0106] If there is a fifth category in the other categories whose proportion is greater than or equal to the preset proportion threshold, the fourth category that meets the combination condition is determined from the fifth category.

[0107] In a possible implementation manner, objects in the at least one object combination all belong to the same entity.

[0108] In a possible implementation, the apparatus 300 further includes:

[0109] A scoring unit is configured to determine a collocation score for each object combination in the at least one object combination based on the correlation degree of each object combination in the at least one object combination, wherein the correlation degree of the object combination represents the proportion or number of times that objects belonging to the object combination in the target entity are marketed in combination, and the collocation score is used to guide the display and / or publication of the corresponding object combination.

[0110] It should be noted that the specific implementation of the device 300 and the technical effects achieved can be found in the relevant description of the method shown in FIG1 .

[0111] In addition, an embodiment of the present application also provides an electronic device, which includes a processor and a memory: the memory is used to store instructions or computer programs; the processor is used to execute the instructions or computer programs in the memory, so that the electronic device executes any implementation of the method provided in the embodiment of the present application.

[0112] Referring to FIG4 , a schematic diagram of the structure of an electronic device 400 suitable for implementing embodiments of the present disclosure is shown. Terminal devices in embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The electronic device shown in FIG4 is merely an example and should not limit the functionality or scope of use of embodiments of the present disclosure.

[0113] As shown in Figure 4, electronic device 400 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. Various programs and data required for the operation of electronic device 400 are also stored in RAM 403. Processing device 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0114] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. Although FIG4 shows the electronic device 400 with various devices, it should be understood that not all of the devices shown are required to be implemented or present. More or fewer devices may alternatively be implemented or present.

[0115] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 409, or installed from the storage device 408, or installed from the ROM 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0116] The electronic device provided by the embodiment of the present disclosure and the method provided by the above embodiment belong to the same inventive concept. For technical details not fully described in this embodiment, please refer to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0117] An embodiment of the present application further provides a computer-readable medium, in which instructions or computer programs are stored. When the instructions or computer programs are executed on a device, the device executes any implementation of the method provided in the embodiment of the present application.

[0118] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0119] In some embodiments, the client and server can communicate using any currently known or later developed network protocol, such as HTTP (Hypertext Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or later developed network.

[0120] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0121] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device can perform the method.

[0122] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving 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 may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0124] The units involved in the embodiments described in this disclosure may be implemented in software or hardware, wherein the name of a unit / module does not, in some cases, limit the unit itself.

[0125] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0126] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0127] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems or devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0128] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0129] It should also be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0130] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0131] It should be noted that in the embodiments of the present application, user sensitive information is not involved, and user-related information is obtained, used and determined after user authorization. In one example, before obtaining user-related information, the corresponding interface displays prompt information related to obtaining data use authorization. This prompt information is based on relevant laws and regulations and informs the user of the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure in an appropriate manner, so that the user can determine whether to agree to the authorization based on the prompt information. It can be understood that the above-mentioned notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation method of this disclosure. Other methods that meet relevant laws and regulations can also be applied to the implementation method of this disclosure.

[0132] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating an object combination, comprising: According to the category information of each object, determine the category to which each object belongs; At least one object combination is generated based on a category association relationship library and the categories to which each object belongs, wherein each category association relationship in the category association relationship library includes at least two categories that meet a combination condition determined based on historical interaction result information, and the interaction result information is used to reflect the historical interaction between the at least two categories, and the categories to which the at least two objects included in each object combination in the at least one object combination belong match a category association relationship in the category association relationship library.

2. The method according to claim 1, wherein: The category association relationship library includes a first category association relationship, the first category association relationship includes a first category and a second category, and generating at least one object combination according to the category association relationship library and the category to which each object belongs includes: Determine a first object set consisting of objects belonging to the first category among the objects; Determine a second object set consisting of objects belonging to the second category among the objects; The at least one object combination is generated according to the first object set and the second object set, and each object combination in the at least one object combination includes any one object in the first object set and any one object in the second object set.

3. The method according to claim 1 or 2, wherein: The process of constructing the category association relationship library includes: According to the interaction behavior between the object of the third category and the category of other objects, the interaction result information corresponding to the third category is obtained, and the interaction result information corresponding to the third category includes the number of positive interactions and the reverse interaction ratio between the third category and the other categories, the positive interaction refers to the time when the same user interacts with the object belonging to the third category at a preset time before the time when the user interacts with the object belonging to the other category, and the interaction corresponding to the object belonging to the third category and the interaction corresponding to the object belonging to the other category are two adjacent interaction behaviors, the reverse interaction ratio refers to the number of times that the previous interaction behavior before the interaction corresponding to the object belonging to the other category is the interaction behavior corresponding to the object belonging to the third category, and the ratio of the number of times that the interaction behavior corresponding to the object belonging to the third category occurs, and the interval time between the time when the interaction corresponding to the object belonging to the other category occurs and the time when the interaction corresponding to the object belonging to the third category occurs is less than or equal to the preset time; determining, according to the number of positive interactions and the reverse interaction ratio, a fourth category that meets the combination condition with the third category, wherein the fourth category belongs to at least one of the other categories; The third category and each of the fourth categories are recorded as a category association relationship in the category association relationship library.

4. The method according to claim 3, wherein: The determining, according to the number of positive interactions and the reverse interaction ratio, a fourth category that meets the combination condition with the third category includes: Determine the third category and each of the other categories according to the number of positive interactions and the reverse interaction ratio The association weight of According to the association weights of the other categories, the fourth category that meets the combination condition is determined, and the combination condition includes: a preset number of the other categories with the largest association weights, or, the other categories with association weights greater than a preset weight threshold are determined as the fourth category.

5. The method according to claim 4, wherein: The determining, according to the association weights of the other categories, the fourth category that satisfies the combination condition includes: If there is a fifth category in the other categories whose proportion is greater than or equal to the preset proportion threshold, the fourth category satisfying the combination condition is determined from the fifth category.

6. The method according to any one of claims 1 to 5, wherein: The objects in the at least one object group all belong to the same entity.

7. The method according to claim 6, wherein: The method further comprises: According to the association degree of each object combination in the at least one object combination, a collocation score of each object combination in the at least one object is determined, the association degree of the object combination represents the proportion or number of times that the objects belonging to the object combination in the target entity are marketed in combination, and the collocation score is used to guide the display and / or publication of the corresponding object combination.

8. A device for generating an object combination, comprising: A determination unit, used to determine the category to which each object belongs according to the category information of each object; A generating unit is used to generate at least one object combination according to a category association relationship library and the categories to which each object belongs, wherein each category association relationship in the category association relationship library includes at least two categories that meet a combination condition determined based on historical interaction result information, and the interaction result information is used to reflect the historical interaction between the at least two categories, and the categories to which the at least two objects included in each object combination in the at least one object combination belong match a category association relationship in the category association relationship library.

9. An electronic device, comprising: Processor and memory; The memory is used to store instructions or programs; The processor is used to execute the instructions or programs in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

10. A readable medium storing instructions or programs, which, when executed on a processor, enable the processor to execute the method according to any one of claims 1 to 7.

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