CN112232915A - Commodity recommendation method and device combining RPA and AI - Google Patents
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Abstract
The invention provides a commodity recommendation method and device combining RPA and AI, relating to the technical field of AI and RPA, wherein the method comprises the following steps: acquiring current shopping scene information and determining recommended scene information corresponding to the current shopping scene information; determining a corresponding recall strategy set based on the recommendation scene information, wherein the recall strategy set comprises at least one recall strategy; determining candidate recommended commodities according to at least one recall strategy contained in the recall strategy set; and determining the target recommended commodity based on the candidate recommended commodity, generating and outputting recommendation data of the target recommended commodity. Therefore, according to different shopping scene information, different recall strategies are adapted to recall the candidate recommended commodities for recommendation, and the conversion rate of the commodities recommended to the user is guaranteed.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to the fields of Artificial Intelligence (AI) and Robot Process Automation (RPA), and particularly relates to a commodity recommendation method and device combining the RPA and the AI.
Background
Robot Process Automation (RPA) is a Process task that simulates human operations on a computer by specific "robot software" and executes automatically according to rules.
Artificial Intelligence (Artificial Intelligence), abbreviated in english as AI. The method is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence, a field of research that includes robotics, speech recognition, image recognition, natural language processing, and expert systems.
With the development of computer technology, online shopping becomes a mainstream consumption mode, and therefore, functional services provided by applications related to online shopping are more diversified, wherein the applications related to online shopping can recommend commodities for users, so as to improve the purchase conversion rate of products.
In the related art, related applications extract search keywords historically purchased by a user, determine other keywords having a high degree of association with the search keywords, and then recommend a product to the user according to the other keywords.
However, the above method of recommending a product according to a search term historically purchased by a user completely depends on the historical purchase of the product by the user to recommend the product, which results in a high similarity between the recommended product and the product historically purchased by the user and a low conversion rate of the purchase of the recommended product.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first objective of the present invention is to provide a commodity recommendation method combining RPA and AI, so as to adapt different recall strategies according to different shopping scenario information to recall candidate recommended commodities for recommendation, thereby ensuring a conversion rate of commodities recommended to a user.
A second object of the present invention is to provide a commodity recommendation apparatus combining RPA and AI.
A third object of the invention is to propose an electronic device.
A fourth object of the invention is to propose a non-transitory computer-readable storage medium.
To achieve the above object, an embodiment of a first aspect of the present invention provides a method for recommending commodities by combining RPA and AI, including: acquiring current shopping scene information and determining recommended scene information corresponding to the current shopping scene information; determining a corresponding recall strategy set based on the recommendation scene information, wherein the recall strategy set comprises at least one recall strategy; determining candidate recommended commodities according to at least one recall strategy contained in the recall strategy set; and determining a target recommended commodity based on the candidate recommended commodity, generating and outputting recommendation data of the target recommended commodity.
In order to achieve the above object, a second embodiment of the present invention provides an article recommendation device combining RPA and AI, including: the system comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for acquiring current shopping scene information and determining recommendation scene information corresponding to the current shopping scene information; a second determining module, configured to determine a corresponding recall policy set based on the recommended scenario information, where the recall policy set includes at least one recall policy; the third determining module is used for determining candidate recommended commodities according to at least one recall strategy contained in the recall strategy set; and the generation module is used for determining a target recommended commodity based on the candidate recommended commodity, generating and outputting recommendation data of the target recommended commodity.
To achieve the above object, an embodiment of a third aspect of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the article recommendation method combining RPA and AI as described in the embodiment of the first aspect is implemented.
In order to achieve the above object, a fourth embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for recommending an article by combining an RPA and an AI as described in the first embodiment.
The technical scheme provided by the embodiment of the invention at least has the following beneficial technical effects:
the method comprises the steps of obtaining current shopping scene information, determining recommended scene information corresponding to the current shopping scene information, determining a corresponding recall strategy set based on the recommended scene information, wherein the recall strategy set comprises at least one recall strategy, further determining candidate recommended commodities according to the at least one recall strategy contained in the recall strategy set, finally determining target recommended commodities based on the candidate recommended commodities, generating and outputting recommended data of the target recommended commodities. Therefore, according to different shopping scene information, different recall strategies are adapted to recall the candidate recommended commodities for recommendation, and the conversion rate of the commodities recommended to the user is guaranteed.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of a commodity recommendation method combining an RPA and an AI according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a commodity recommendation method combining an RPA and an AI according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of another commodity recommendation method combining an RPA and an AI according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating a further commodity recommendation method combining an RPA and an AI according to an embodiment of the present invention;
fig. 5 is a flowchart illustrating a further commodity recommendation method combining an RPA and an AI according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a commodity recommendation device combining an RPA and an AI according to an embodiment of the present invention; and
fig. 7 is a schematic structural diagram of another commodity recommendation device combining an RPA and an AI according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Hereinafter, a commodity recommendation method and apparatus combining an RPA and an AI according to an embodiment of the present invention will be described with reference to the accompanying drawings.
Aiming at the technical problems that the recommended commodities are not accordant with the actual purchasing requirements of the users and the conversion rate of the recommended commodities is not high due to the fact that the commodities are recommended based on the keywords of the historical commodities purchased by the users in the background technology, the invention provides an optimized recall mode, and the candidate recommended commodities are recalled for recommendation by adapting to different recall strategies according to different shopping scene information, so that the conversion rate of the commodities recommended to the users is ensured.
Specifically, fig. 1 is a flowchart illustrating a commodity recommendation method combining an RPA and an AI according to an embodiment of the present invention. The method is applied to electronic equipment, the electronic equipment provides commodity recommendation service based on the robot flow automation RPA, and relevant information is determined based on natural language processing NLP when the commodity recommendation service based on the robot flow automation RPA is used.
As shown in fig. 1, the commodity recommendation method includes the steps of:
In this embodiment, the current shopping scenario information is acquired, and the current shopping information reflects the shopping intention of the user, so that the corresponding recommended scenario information can be determined according to the current shopping information, so as to further summarize a recall policy and the like corresponding to the shopping intention of the user based on the recommended scenario information.
It should be noted that, in different application scenarios, the current shopping scenario information is different, and the recommendation scenario information determined according to the current shopping scenario information is also different, and the following example is illustrated:
example one:
in this example, the page information currently browsed by the user is determined, where the page information may include page identification information, page content information, page type information, and the like, and the recommendation scene information is determined according to the page information, where the recommendation scene information may be identification information that arbitrarily distinguishes different recommendation scene information, such as numbers, letters, and text descriptions, and where the recommendation scene information may include "recommend a scene according to details of a commodity", "recommend a scene according to package remaining amount", and the like, when the recommendation scene information is a text description.
In this embodiment, the page identification information, the page content information, the page type information, and the like in the page information may be extracted based on a Natural Language Processing (NLP) technique. The page information or the like may also be recognized according to an OCR algorithm according to optical character recognition.
Example two:
in this example, the page information is obtained, the preset corresponding relationship is queried, and the recommended scene information corresponding to the page information is obtained, for example, when the page information is a page identifier, if the page identifier corresponding to the page browsed by the user is a flow allowance check page identifier, the preset corresponding relationship is queried, and the determined corresponding recommended scene information is a recommended scene according to the package flow allowance.
In this embodiment, a deep learning model may be trained based on the NLP technology, where the deep learning model has the input of page information and the output of recommended scene information.
Example three:
in this example, the current shopping scenario information includes current holiday information, for example, if it is current christmas, the recommended scenario is a christmas-based recommended scenario, and the like.
And 102, determining a corresponding recall strategy set based on the recommendation scene information, wherein the recall strategy set comprises at least one recall strategy.
And 103, determining candidate recommended commodities according to at least one recall strategy contained in the recall strategy set.
Specifically, a corresponding recall strategy set is determined based on recommendation scene information, wherein the recall strategy set comprises at least one recall strategy, and candidate recommended commodities are determined according to the at least one recall strategy contained in the recall strategy set, wherein the recommendation scene information is related to the purchase intention of the user, so that the recalled candidate recommended commodities can be ensured to be matched with the purchase intention of the user according to the at least one recall strategy determined according to the recommendation scene.
The recall strategies in the recall strategy set can comprise sales rate ranking list recalls according to certain commodity types, commodity recalls with high similarity to historically purchased commodities, sales rate ranking list recalls of commodities in provinces where the users are located and the like.
As a possible implementation manner, a corresponding relationship between recommended scene information and a recall policy set may be preset, and in this embodiment, a deep learning model may be trained based on an NLP technique, where an input of the deep learning model is the recommended scene information and an output of the deep learning model is the recall policy set. Therefore, the corresponding relation is inquired, and a recall strategy set corresponding to the recommended scene information is obtained. For example, in the correspondence, the recall policy set corresponding to the recommendation scene information "recommendation scene of details of commodity" is "sales ranking list, similar commodities to the currently browsed commodity", and the like, and for example, in the correspondence, the recall policy set corresponding to the recommendation scene information "package remaining amount recommendation scene" is "sales ranking list, similar commodities to the currently browsed commodity, user package remaining amount recall", and the like. The user package allowance recalling means recalling the commodity according to the package allowance information of the user and the flow consumption of the commodity in the historical order, so that the flow consumed by the recalled commodity similar to the commodity in the historical order of the user is not more than the package allowance of the user. For another example, in the corresponding relationship, the recall policy set may also be identified by identification information, for example, the stored recommendation scenario information is 0, and the recall policy set corresponding to 0 is a.
As another possible implementation manner, considering that the purchase demand of the user can change along with the change of the time period, in order to further grasp the purchase intention of the user, the corresponding recall strategy set is determined by combining the time strategy.
In this embodiment, current time information may be obtained, where the time information may include time period information (such as the middle of a month, the beginning of a month, and the end of a month) located in a month, and may also be distance duration from a holiday and the like (such as how long the holiday is since the last year), and a corresponding recall policy set is determined in combination with the time information and the recommended scenario information.
For example, a time period corresponding to the time information may be determined, for example, the current time period is determined according to the time information of the electronic device to which the current application is applied, a preset corresponding relationship is queried, and a recall policy set corresponding to the time period and the scene information is obtained.
In a possible implementation manner, determining a corresponding recall policy set based on the time information and the scenario information includes:
inquiring a preset corresponding relation, and acquiring a recall strategy set corresponding to the recommended scene information;
and adjusting parameters in a recall strategy set corresponding to the recommended scene information based on the time information to generate a recall strategy set corresponding to the time information and the recommended scene information.
In an embodiment, the time information may be used to adjust a collection time period parameter of the recalled commodities in the recall policy determined based on the recommendation scenario information, for example, when the time period is the beginning of the month and the recall policy is a sales ranking list recall, the finally determined recall policy is the sales ranking list commodities counted for the past 48 hours according to the recall statistical period, and when the recall policy is the sales ranking list recall at the end of the month, the finally determined recall policy is the sales ranking list commodities counted for the past 24 hours.
And 104, determining the target recommended commodity based on the candidate recommended commodity, generating and outputting recommendation data of the target recommended commodity.
Specifically, the target recommended commodity is determined based on the candidate recommended commodity, so that recommendation data of the target recommended commodity is generated and output. In an embodiment of the present invention, after the target recommended product is determined, a product identifier of the target recommended product may also be determined, whether a candidate recommended product includes a duplicate product may be determined according to information such as the product identifier, and if the candidate recommended product includes the duplicate product, duplication removal is performed on the target recommended product.
The recommendation data of the target commodity may be an ordered list including the target commodity, or a list including the target commodity.
In consideration of some situations, target recommended commodities may be fewer, and in this case, in order to improve the commodity recommendation experience, after the target recommended commodities are acquired, whether the number of the target recommended commodities is smaller than the number of preset recommended commodities is determined, where the number of the preset recommended commodities may be set according to the screen size and the like of the electronic device of the user to be recommended, or may be customized by the user.
If the number of the commodities is less than the preset recommended commodity number, calculating the number difference between the preset recommended commodity number and the target recommended commodity number, and acquiring a commodity sales ranking list corresponding to the recommendation scene information, wherein the province of the user to be recommended corresponding to the recommendation scene information can be acquired, the commodity sales ranking list under the province is determined according to the province of the user, or alternatively, it can be understood that the corresponding relation of the commodity sales ranking list corresponding to the recommendation scene information is established in advance, the corresponding commodity sales ranking list is determined based on the corresponding relation, the supplementary recommended commodities corresponding to the number difference are acquired in the commodity sales ranking list according to the top-down ranking order, the supplementary recommended commodities and the target recommended commodity are recommended and output, and it can be understood that the top-down ranking of the ranking list is the ranking mode of the supplementary recommended commodities and the target recommended commodities from the front to the back, the supplementary recommended goods may be placed before the target recommended goods, or placed after the target recommended goods, or the supplementary recommended goods may be reordered from the target recommended goods.
In different application scenarios, the manner of generating the target recommended product information based on the candidate recommended products is different, and the following example is given:
example one:
in this example, a corresponding filtering policy set is determined based on the recommendation scenario information, where the filtering policy set includes at least one filtering policy, and the at least one filtering policy may include filtering based on a province and a city where the user is located, filtering based on a point balance of the user, and filtering based on a payment method supported by the user, or at least one filtering policy including a filtering policy based on a commodity attribute, where the commodity attribute includes a price, a province, and the like of the commodity.
In some possible examples, a corresponding relationship between the recommended scene information and the filtering policy set may be pre-constructed, and the filtering policy set corresponding to the recommended scene information may be determined by querying the preset corresponding relationship. In this embodiment, a deep learning model may be trained based on the NLP technology, where the input of the deep learning model is recommended scene information, and the output is a strategy set.
Further, filtering the candidate recommended commodities according to at least one filtering strategy contained in the filtering strategy set, and determining the target recommended commodity based on the filtered candidate recommended commodities.
For example, when the at least one filtering strategy comprises filtering based on sales volume, the goods with low sales volume in the candidate recommended goods are filtered.
In the actual implementation process, in order to enable the target recommended goods to be closer to the purchase demand of the user, when the candidate recommended goods are filtered, the characteristics of the user to be recommended can be obtained, and the personalized filtering strategy set of the user is generated based on the characteristics of the user to be recommended and the filtering strategy set, so that the candidate recommended goods are filtered based on the personalized filtering strategy set of the user. The user characteristics to be recommended comprise the province of the user, the account balance of the user, the order history information of the user and the like.
For example, when the filtering policy set includes the filtering policy arranged according to the sales volume and the to-be-recommended user characteristic is the user balance 400, the generated personalized filtering policy set includes the personalized filtering policy for filtering out the commodities lower than 400 according to the sales volume.
Example two: in this example, a corresponding ranking policy set is determined based on the recommended scene information, where the ranking policy set includes at least one ranking policy, in some possible examples, a preset corresponding relationship is queried, the ranking policy corresponding to the recommended scene information is determined, further, according to the at least one ranking policy, the candidate recommended commodities are ranked, and based on the ranked candidate recommended commodities, the target recommended commodities are determined, for example, the top preset number of the ranked candidate recommended commodities is directly used as the target recommended commodity, and the like.
Certainly, under some conditions, at least one ranking policy determined according to the recommendation scenario information may not be adapted to the current candidate recommended product, for example, for a product ranked according to the similarity to the product purchased by the user history, if the similarity to the product purchased by the user history in the candidate recommended product is lower, it is obvious that the corresponding ranking policy cannot be adopted for ranking, therefore, the ranking condition corresponding to each ranking policy in the at least one ranking policy is obtained, the reference information corresponding to the ranking condition in the target recommended product is extracted, for example, if the ranking condition is that the candidate recommended product whose similarity to the product purchased by the user history is greater than the preset threshold is greater than a certain value in the total number of the candidate recommended products, the reference information is the proportion of the candidate recommended product whose similarity to the product purchased by the user history is greater than the preset threshold, and then, matching the reference information with the sorting conditions corresponding to each sorting strategy, and deleting the sorting strategy with failed matching in at least one sorting strategy.
For more clear description of how to sort the candidate recommended commodities according to the sorting strategy, the following description is provided with specific scenarios:
scene one:
in this scenario, the at least one ordering policy includes a first ordering policy.
Then, as shown in fig. 2, the above step of ranking the candidate recommended goods according to at least one ranking policy includes:
Specifically, whether a preset first ordering policy is included in at least one ordering policy may be determined according to policy content information or identification information of the at least one ordering policy, if the preset first ordering policy is included, commodity purchasing information of a user is obtained, where the commodity purchasing information may include a type of a commodity, a quantity of the commodity, a time for purchasing the commodity, and the like, for example, commodity information purchased by the user within a current preset time period is obtained, and the commodity purchasing information may be obtained by calling a historical purchase order of the user, for example, extracting commodity information in the historical purchase order based on an NLP technique, and determining a first consumption characteristic of the commodity purchasing information, where the first consumption characteristic corresponds to an effective usage duration, a remaining amount, and the like of the historically purchased commodity (the remaining amount may be determined according to a previously collected consumption speed of the user), Time to the bottom of the month, etc.
Specifically, the second consumption characteristics of the candidate recommended item are extracted, and the second consumption characteristics may include item unit price, usable time length, and the like of the target recommended item.
And 203, inputting the first consumption characteristics and the second consumption characteristics into a pre-constructed scoring model, and obtaining a scoring result of the target recommended commodity.
It can be understood that a scoring model is constructed in advance, and the scoring model can be obtained through training of a large amount of sample data. The pre-constructed scoring model can obtain a scoring result of the corresponding target recommended commodity according to the first consumption characteristic and the second consumption characteristic, wherein the higher the scoring result is, the higher the recommendability of the candidate recommended commodity is.
In an embodiment of the present invention, the first consumption characteristic and the second consumption characteristic may be multiple, and when multiple, the corresponding weight may be set according to the degree of contribution of each first consumption characteristic and the second consumption characteristic to the ranking, the product value of each first consumption characteristic and the corresponding weight is used as the first consumption characteristic of the final input scoring model corresponding to the first consumption characteristic, and similarly, the product value of each second consumption characteristic and the corresponding weight is used as the first consumption characteristic of the final input scoring model corresponding to the second consumption characteristic.
And step 204, sorting the candidate recommended commodities according to the scoring result.
It can be understood that the higher the scoring result is, the earlier the ranking of the corresponding candidate recommended merchandise is, and in this embodiment, the candidate recommended merchandise is ranked according to the scoring result.
Scene two:
in this scenario, the at least one ordering policy includes a first ordering policy.
Then, as shown in fig. 3, the above step of ranking the candidate recommended goods according to at least one ranking policy includes:
Specifically, it may be determined whether at least one ranking policy includes a preset second ranking policy according to policy content information or identification information of the at least one ranking policy, where the second ranking policy may be understood as DNN ranking, and if the at least one ranking policy includes the second ranking policy, first product attribute information of a product purchased by a user is obtained, where the first product attribute information may include a product identification, a product price, a product purchase time, and the like of a product purchased by the user in history.
The second item attribute information may include an item identifier of a user's historical purchase item, an item price, an item purchase time, and the like, corresponding to the first item attribute.
The preset ranking algorithm can be a deep learning algorithm and the like, the preset ranking algorithm can be obtained through learning according to a large amount of sample data, the similarity probability of the first commodity attribute and the second commodity attribute is calculated according to the preset ranking algorithm, and the candidate recommended commodities are ranked according to the similarity probability, wherein obviously, the higher the similarity probability is, the higher the ranking is.
The above ordering is considered equally for each candidate recommended commodity, and in some scenes, a background operator hopes that the ordering of some commodities recalled by the recall strategy is advanced for the promotion of the commodities. The invention also provides a processing mode for intervening the sorting of the candidate commodities.
Specifically, as shown in fig. 4, after ranking the candidate recommended goods, the method may further include:
It can be understood that each recall strategy in the recall strategy set has a recall attribute setting, and the attribute can be manually modified by technical personnel according to promotion needs, wherein if the candidate recommended goods desired to be recalled by a certain recall strategy are ranked in the front, the attribute of the corresponding recall strategy can be set as a pre-ranking insertion attribute, wherein the pre-ranking insertion attribute can be a string of fixed codes, characters and the like.
And judging whether the recall strategy set contains a target recall strategy with a preset sorting pre-interpolation attribute so as to determine whether a person is in the condition of intervention and pre-sorting.
Specifically, if the target recall strategy is included, the target candidate recommended commodities corresponding to the target recall strategy are determined from the candidate recommended commodities, and the candidate recommended commodities recalled by the target recall strategy from the candidate recommended commodities are screened out.
In step 403, the candidate recommended goods with the ranking order before the target candidate recommended goods in the ranked candidate recommended goods are adjusted to be behind the target candidate recommended goods.
Specifically, in the sorted candidate recommended commodities, the candidate recommended commodities whose sorting order is before the target candidate recommended commodity are adjusted to be behind the target candidate recommended commodity, that is, in the sorting result of the candidate recommended commodities, all the target candidate recommended commodities are in front of the target candidate recommended commodity to be sorted, and when there are a plurality of target candidate recommended commodities, the order of the plurality of target candidate recommended commodities may be random.
Example three:
in this example, the candidate recommended commodities may be filtered, and then the target recommended commodities may be obtained, where the filtering manner may be the manner shown in the first example, and after the filtering, the target recommended commodities may be sorted, where the sorting manner may be sorting according to the manner shown in the second example, and recommendation data including the target recommended commodities is generated and output according to the sorted target recommended commodities.
It should be understood that the product recommendation operation method shown in each of the above embodiments may be configured as a single processing module, and the processing modules of the different embodiments may cooperate to form a flexible product recommendation system, which is described below with reference to a specific application scenario, where the processing manner involved in this scenario is only one possible example and is intended to be used for explaining the product recommendation method of the embodiment of the present invention, and is not to be construed as limiting the present invention.
Referring to fig. 5, in practical application, a corresponding recall policy set may be determined according to the determined recommendation scenario information, where the recall policy set includes N recall policies, where N is an integer greater than 1, and further the recall policy set may be distributed by a recall policy distribution module, where in practical application, in order to ensure recall efficiency, a recalled candidate recommended product may only include a product identifier, and therefore, referring to fig. 5, a product information completion module may be further provided, where the product information completion module may communicate with the database, and complete product information of the candidate recommended product according to the product identifier.
Further, the candidate recommended commodities are sent to a filtering module, and filtering strategy combination can be determined in the filtering module according to user attributes (such as historical order information of the user) and recommended scene information, so that filtering of the candidate recommended commodities is achieved, and the target recommended commodities are generated.
After the target recommended commodities are obtained, the target recommended commodities are sent to a universal sorting module, the combination of sorting strategies can be determined by combining the user characteristics of the users to be recommended and the current recommendation scene information, the target recommended commodities are sorted based on the sorting strategies, then the sorted target recommended commodities are sent to an operation and completion module, on one hand, whether the proficiency of the target recommended commodities is larger than the preset recommended commodity number is judged, and if the proficiency of the target recommended commodities is smaller than the preset recommended commodity number, the target recommended commodities are completed according to a purchase and sales ranking list of provinces where the users to be recommended are located in the top-down sequence. On the other hand, if there is a commodity paid for promotion by the merchant in the target recommended commodities, the corresponding target recommended commodity can be referred to the front end of the sorting.
And finally, sending the sorted target recommended commodities to the user to be recommended after passing through the operation and completion module.
To sum up, according to the commodity recommendation method combining the RPA and the AI in the embodiment of the present invention, current shopping scene information is obtained, recommendation scene information corresponding to the current shopping scene information is determined, a corresponding recall policy set is determined based on the recommendation scene information, where the recall policy set includes at least one recall policy, further, candidate recommended commodities are determined according to the at least one recall policy included in the recall policy set, and finally, target recommended commodities are determined based on the candidate recommended commodities, and recommendation data of the target recommended commodities are generated and output. Therefore, according to different shopping scene information, different recall strategies are adapted to recall the candidate recommended commodities for recommendation, and the conversion rate of the commodities recommended to the user is guaranteed.
In order to implement the above embodiments, the present invention further provides a commodity recommendation device combining RPA and AI. The device is applied to electronic equipment, and the electronic equipment provides commodity recommendation service based on Robot Process Automation (RPA). Fig. 6 is a schematic structural diagram of a product recommendation device combining an RPA and an AI according to the present invention, as shown in fig. 6, the product recommendation device includes: a first determination module 10, a second determination module 20, a third determination module 30, and a generation module 40, wherein,
the first determining module 10 is configured to acquire current shopping scenario information and determine recommendation scenario information corresponding to the current shopping scenario information.
In an embodiment of the present invention, the first determining module 10 is specifically configured to determine page information currently browsed by a user; and determining recommended scene information based on the page information.
In the embodiment, the first determining module 10 determines the category of the to-be-recommended goods based on the page information;
and determining recommendation scene information based on the category of the goods to be recommended.
The second determining module 20 is configured to determine a corresponding recall policy set based on the recommendation context information, where the recall policy set includes at least one recall policy.
And the third determining module 30 is configured to determine the candidate recommended product according to at least one recall policy included in the recall policy set.
In an embodiment of the present invention, the third determining module 30 is specifically configured to query a preset corresponding relationship, and obtain a recall policy set corresponding to the recommended scene information.
In an embodiment of the present invention, the third determining module 30 is specifically configured to obtain current time information;
and determining a corresponding recall strategy set based on the time information and the recommended scene information.
In this embodiment, the third determining module 30 is specifically configured to determine a time period corresponding to the time information;
and inquiring a preset corresponding relation, and acquiring a recall strategy set corresponding to the time period and the scene information.
In an embodiment of the present invention, the third determining module 30 is specifically configured to determine a corresponding filtering policy set based on the recommended scenario information, where the filtering policy set includes at least one filtering policy;
and filtering the candidate recommended commodities according to at least one filtering strategy contained in the filtering strategy set, and determining the target recommended commodity based on the filtered candidate recommended commodities.
In an embodiment of the present invention, the third determining module 30 is specifically configured to obtain a feature of a user to be recommended;
generating an individualized filtering strategy set of the user based on the characteristics of the user to be recommended and the filtering strategy set;
and filtering the candidate recommended commodities based on the personalized filtering strategy set of the user.
In an embodiment of the present invention, the third determining module 30 is specifically configured to determine a corresponding ranking policy set based on the recommendation context information, where the ranking policy set includes at least one ranking policy;
sorting the candidate recommended commodities according to at least one sorting strategy;
and determining the target recommended commodity based on the sorted candidate recommended commodities.
In this embodiment, the third determining module 30 is specifically configured to obtain a ranking condition corresponding to each ranking policy in at least one ranking policy;
extracting reference information corresponding to the sorting condition in the target recommended commodity;
matching the reference information with the sorting conditions corresponding to each sorting strategy;
and deleting the ranking strategies which fail to be matched from at least one ranking strategy.
In this embodiment, the third determining module 30 is specifically configured to, when the at least one ranking policy includes a first ranking policy, acquire purchased commodity information of a user, and determine a first consumption characteristic of the purchased commodity information;
extracting second consumption characteristics of the candidate recommended commodities;
inputting the first consumption characteristic and the second consumption characteristic into a pre-constructed scoring model to obtain a scoring result of the target recommended commodity;
and sorting the candidate recommended commodities according to the scoring result.
In this embodiment, the third determining module 30 is specifically configured to, when the at least one ranking policy includes the second ranking policy, acquire browsing and purchasing commodity information of the user, and extract a first commodity attribute feature of the browsing and purchasing commodity information;
extracting second commodity attribute characteristics of the candidate recommended commodities;
and calculating the similarity probability of the first commodity attribute feature and the second commodity attribute feature according to a preset sorting algorithm, and sorting the candidate recommended commodities according to the similarity probability.
In this embodiment, as shown in fig. 7, on the basis of fig. 6, the apparatus further includes: a determining module 50, wherein the determining module 50 determines whether the recall policy set includes a target recall policy of a preset sort pre-insertion attribute, and the third determining module 30 is specifically configured to determine a target candidate recommended commodity corresponding to the target recall policy from the candidate recommended commodities if the target recall policy is included;
and in the sorted candidate recommended commodities, adjusting the candidate recommended commodities with the sorting order being before the target candidate recommended commodity to be behind the target candidate recommended commodity.
And the generating module 40 is configured to determine a target recommended commodity based on the candidate recommended commodity, generate and output recommendation data of the target recommended commodity.
In an embodiment of the present invention, the generating module 40 is specifically configured to query the current database according to the product identifier of the candidate recommended product, and obtain information of the candidate recommended product corresponding to the product identifier;
and determining the target recommended commodity based on the candidate recommended commodity information.
In an embodiment of the present invention, the generating module 40 is specifically configured to determine whether the number of the target recommended commodities is less than a preset recommended commodity number;
if the number of the target recommended commodities is less than the preset recommended commodity number, calculating the number difference between the preset recommended commodity number and the target recommended commodity number;
acquiring a commodity sales ranking list corresponding to the recommendation scene information;
acquiring supplementary recommended commodities corresponding to the quantity difference in a commodity sales ranking list according to a top-down ranking sequence;
and outputting the supplementary recommended commodity and the target recommended commodity recommendation.
In an embodiment of the present invention, the generating module 40 is specifically configured to determine whether the target recommended product includes a duplicate product;
if duplicate goods are included, duplicate goods are de-duplicated.
In an embodiment of the present invention, the generating module 40 is specifically configured to filter the candidate recommended commodities to obtain target recommended commodities;
ordering the target recommended commodities;
and generating and outputting recommendation data according to the sorted target recommended commodities.
It should be noted that the above explanation of the embodiment of the method for recommending a commodity by combining the RPA and the AI is also applicable to the commodity recommending device by combining the RPA and the AI of this embodiment, and is not repeated here.
In order to implement the above embodiments, the present invention further provides an electronic device, including: the article recommendation system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein when the processor executes the computer program, the article recommendation method combining the RPA and the AI in the above embodiments is realized.
In order to achieve the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium in which instructions, when executed by a processor, enable execution of the merchandise recommendation method combining RPA and AI in the above embodiments.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (23)
1. A commodity recommendation method combining RPA and AI is characterized by comprising the following steps;
acquiring current shopping scene information and determining recommended scene information corresponding to the current shopping scene information;
determining a corresponding recall strategy set based on the recommendation scene information, wherein the recall strategy set comprises at least one recall strategy;
determining candidate recommended commodities according to at least one recall strategy contained in the recall strategy set;
and determining a target recommended commodity based on the candidate recommended commodity, generating and outputting recommendation data of the target recommended commodity.
2. The method of claim 1, wherein the obtaining current shopping scenario information and determining recommendation scenario information corresponding to the current shopping scenario information comprises:
determining the information of the current browsed page of the user according to an Optical Character Recognition (OCR) algorithm;
and determining recommended scene information based on the page information.
3. The method of claim 1, wherein said determining a target recommended good based on the candidate recommended good comprises:
inquiring a current database according to the commodity identification of the candidate recommended commodity, and acquiring candidate recommended commodity information corresponding to the commodity identification;
and determining a target recommended commodity based on the candidate recommended commodity information.
4. The method of claim 1, wherein the determining a corresponding set of recall policies based on the recommendation scenario information comprises:
and inquiring a preset corresponding relation, and acquiring a recall strategy set corresponding to the recommended scene information.
5. The method of claim 1, wherein the determining a corresponding set of recall policies based on the recommendation scenario information comprises:
acquiring current time information;
and determining a corresponding recall strategy set based on the time information and the recommended scene information.
6. The method of claim 5, wherein the determining a corresponding set of recall policies based on the temporal information and recommendation scenario information comprises:
determining a time period corresponding to the time information;
and inquiring a preset corresponding relation, and acquiring a recall strategy set corresponding to the time period and the scene information.
7. The method of claim 5, wherein the determining a corresponding set of recall policies based on the temporal information and recommendation scenario information comprises:
inquiring a preset corresponding relation, and acquiring a recall strategy set corresponding to the recommended scene information;
and adjusting parameters in a recall strategy set corresponding to the recommended scene information based on the time information to generate a recall strategy set corresponding to the time information and the recommended scene information.
8. The method of claim 1, wherein the generating target recommended merchandise information based on the candidate recommended merchandise comprises:
determining a corresponding filtering strategy set based on the recommended scene information, wherein the filtering strategy set comprises at least one filtering strategy;
and filtering the candidate recommended commodities according to at least one filtering strategy contained in the filtering strategy set, and determining target recommended commodities based on the filtered candidate recommended commodities.
9. The method of claim 8, wherein the determining a corresponding set of filtering policies based on the recommendation context information comprises:
and inquiring a preset corresponding relation, and acquiring a filtering strategy set corresponding to the recommended scene information.
10. The method of claim 8, wherein said filtering the candidate recommended goods according to at least one filtering policy included in the set of filtering policies comprises:
acquiring characteristics of a user to be recommended;
generating a personalized filtering strategy set of the user based on the characteristics of the user to be recommended and the filtering strategy set;
and filtering the candidate recommended commodities based on the personalized filtering strategy set of the user.
11. The method of claim 8, wherein the at least one filtering policy comprises a filtering policy based on commodity attributes.
12. The method of claim 1, wherein said determining a target recommended good based on the candidate recommended good comprises:
determining a corresponding sorting strategy set based on the recommendation scene information, wherein the sorting strategy set comprises at least one sorting strategy;
ranking the candidate recommended commodities according to the at least one ranking strategy;
and determining the target recommended commodity based on the sorted candidate recommended commodities.
13. The method of claim 12, wherein the determining a corresponding set of ranking policies based on the recommendation context information comprises:
and inquiring a preset corresponding relation, and acquiring a sorting strategy set corresponding to the recommended scene information.
14. The method of claim 12, wherein prior to said ranking said candidate recommended merchandise according to said at least one ranking strategy, further comprising:
obtaining a sorting condition corresponding to each sorting strategy in the at least one sorting strategy;
extracting reference information corresponding to the sorting condition in the target recommended commodity;
matching the reference information with the sorting condition corresponding to each sorting strategy;
deleting the ranking strategy which fails to be matched from the at least one ranking strategy.
15. The method of claim 12, wherein said ranking said candidate recommended merchandise according to said at least one ranking strategy comprises:
when the at least one sequencing strategy comprises a first sequencing strategy, acquiring purchased commodity information of the user, and determining a first consumption characteristic of the purchased commodity information;
extracting a second consumption characteristic of the candidate recommended commodity;
inputting the first consumption characteristics and the second consumption characteristics into a pre-constructed scoring model to obtain a scoring result of the target recommended commodity;
and sorting the candidate recommended commodities according to the scoring result.
16. The method of claim 12, wherein said ranking said candidate recommended merchandise according to said at least one ranking strategy comprises:
when the at least one sequencing strategy comprises a second sequencing strategy, acquiring the information of the browsed and purchased commodities of the user, and extracting first commodity attribute characteristics of the information of the browsed and purchased commodities;
extracting second commodity attribute characteristics of the candidate recommended commodities;
and calculating the similarity probability of the first commodity attribute feature and the second commodity attribute feature according to a preset sorting algorithm, and sorting the candidate recommended commodities according to the similarity probability.
17. The method of claim 12, further comprising, after said ranking said candidate recommended merchandise:
judging whether the recall strategy set contains a target recall strategy with a preset sorting forward-insert attribute;
if the target recall strategy is included, determining target candidate recommended commodities corresponding to the target recall strategy in the candidate recommended commodities;
and in the sorted candidate recommended commodities, adjusting the candidate recommended commodities with the sorting order being before the target candidate recommended commodity to be behind the target candidate recommended commodity.
18. The method of claim 1, wherein the generating and outputting recommendation data for the target recommended good comprises:
judging whether the number of the target recommended commodities is smaller than the preset recommended commodity number or not;
if the number of the target recommended commodities is less than the preset recommended commodity number, calculating a quantity difference value between the preset recommended commodity number and the target recommended commodity number;
acquiring a commodity sales ranking list corresponding to the recommendation scene information;
acquiring supplementary recommended commodities corresponding to the quantity difference in the commodity sales ranking list according to a top-down ranking sequence;
and generating and outputting recommendation data of the supplementary recommended commodity and the target recommended commodity.
19. The method of claim 1, wherein after said determining a target recommended item based on said candidate recommended items, further comprising:
judging whether the target recommended commodity contains repeated commodities;
and if the duplicate commodities are contained, removing the duplicate commodities.
20. The method of claim 1, wherein the determining a target recommended item based on the candidate recommended items, generating recommendation data for the target recommended item and outputting comprise:
filtering the candidate recommended commodities to obtain the target recommended commodity;
sorting the target recommended commodities;
and generating recommendation data according to the sorted target recommended commodities and outputting the recommendation data.
21. An article recommendation device that combines an RPA and an AI, comprising:
the system comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for acquiring current shopping scene information and determining recommendation scene information corresponding to the current shopping scene information;
a second determining module, configured to determine a corresponding recall policy set based on the recommended scenario information, where the recall policy set includes at least one recall policy;
the third determining module is used for determining candidate recommended commodities according to at least one recall strategy contained in the recall strategy set;
and the generation module is used for determining a target recommended commodity based on the candidate recommended commodity, generating and outputting recommendation data of the target recommended commodity.
22. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the merchandise recommendation method according to any one of claims 1-20 in combination with an RPA and an AI.
23. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method for merchandise recommendation combining RPA and AI according to any one of claims 1-20.
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