Method, device, equipment and medium for intelligent product matching recommendation

Abstract

The present invention discloses a method, device, equipment, and medium for intelligent product pairing recommendation. The method includes: obtaining an image of a currently purchased product; performing feature extraction and analysis on the currently purchased product image based on a general model to obtain matching feature attributes of products that match the currently purchased product; performing similarity matching calculations on the matching feature attributes and product feature attributes in a vector database based on a similarity calculation strategy to filter out product feature attributes from the vector database whose similarity calculation results are greater than a preset threshold, thereby obtaining a first set of product attributes; filtering the first set of product feature attributes based on user behavior annotation data and a retrieval enhancement model to obtain product feature attributes that are close to the user's preferences; obtaining corresponding products from the product database based on the product feature attributes that are close to the user's preferences, and pairing them with the currently purchased product to obtain a pairing solution. This method not only improves the accuracy of product pairing, but also increases the sales conversion rate.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to methods, devices, equipment, and media for intelligent product matching recommendation. Background Art

[0002] For the apparel industry, good clothing combinations can boost sales and drive performance. Therefore, with customer traffic struggling to grow, it's crucial to improve sales and boost performance through product pairing recommendations, both online and offline.

[0003] Most current product pairing recommendations rely on personal experience and aesthetic preferences to create customized clothing combinations for each customer. However, this approach is highly accidental and often struggles to create satisfactory product combinations, hindering sales growth. Another approach uses search-based recommendation algorithms to identify product features and then create combinations based on these features. However, this approach struggles to tailor product combinations to individual customer preferences, resulting in low accuracy.

[0004] Therefore, no effective solution has been proposed for the problem that the accuracy of product matching in existing technical methods is low, which is not conducive to sales and conversion rate. Summary of the Invention

[0005] The embodiments of the present invention provide a method, device, equipment and medium for intelligent product matching recommendation, which aims to solve the problem of low product matching accuracy in existing technical methods, which is not conducive to sales and conversion rate.

[0006] In a first aspect, an embodiment of the present invention provides a method for intelligently recommending product combinations, the method comprising:

[0007] Get the image of the product currently purchased by the user;

[0008] Extracting features of the currently purchased product image based on the trained Tongyi model to obtain characteristic attributes of the currently purchased product image, and storing the characteristic attributes of the currently purchased product image in a preset retrieval enhancement vector database, wherein the characteristic attributes include product category, product size, and product style;

[0009] Performing feature analysis on the feature attributes of the currently purchased product image based on the feature attribute analysis and matching strategy to obtain matching feature attributes of a product that matches the currently purchased product based on the feature attributes of the currently purchased product image;

[0010] performing a similarity matching calculation on the matching characteristic attributes of the product that matches the currently purchased product and the product characteristic attributes in the preset search enhancement vector database based on a similarity calculation strategy to obtain a similarity calculation result, and filtering out product characteristic attributes corresponding to the similarity calculation result that is greater than a preset threshold from the preset search enhancement vector database to obtain a first product attribute set;

[0011] Obtaining user behavior annotation data from a preset storage area, and searching and screening the first set of product feature attributes based on the user behavior annotation data and a search enhancement model to obtain product feature attributes that are close to the user's preferences from the first set of product feature attributes;

[0012] Based on the characteristic attributes of the commodities that are close to the user's preferences, a number of corresponding commodities are obtained from the commodity database, and the number of commodities are matched with the currently purchased commodities to obtain a matching plan to recommend to the corresponding user.

[0013] In a second aspect, an embodiment of the present invention provides a device for intelligently recommending product combinations, comprising:

[0014] An acquisition unit, used to acquire images of products currently purchased by the user;

[0015] a feature extraction unit, configured to extract features from the currently purchased product image based on the trained general meaning model, obtain feature attributes of the currently purchased product image, and store the feature attributes of the currently purchased product image in a preset retrieval enhancement vector database, wherein the feature attributes include product category, product size, and product style;

[0016] a feature analysis unit configured to perform feature analysis on the feature attributes of the currently purchased product image based on a feature attribute analysis and matching strategy, so as to obtain matching feature attributes of a product that matches the currently purchased product based on the feature attributes of the currently purchased product image;

[0017] a screening unit configured to perform a similarity matching calculation on the matching characteristic attributes of the product that matches the currently purchased product and the product characteristic attributes in the preset search enhancement vector database based on a similarity calculation strategy to obtain a similarity calculation result, and to screen the product characteristic attributes corresponding to the similarity calculation result that is greater than a preset threshold from the preset search enhancement vector database to obtain a first product attribute set;

[0018] Obtaining user behavior annotation data from a preset storage area, and searching and screening the first set of product feature attributes based on the user behavior annotation data and a search enhancement model to obtain product feature attributes that are close to the user's preferences from the first set of product feature attributes;

[0019] The combination recommendation unit is used to obtain corresponding products from the product database based on the product feature attributes that are close to the user's preferences, and to match the products with the currently purchased products to obtain a combination plan to recommend to the corresponding user.

[0020] In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method for intelligent product matching recommendation described in the first aspect is implemented.

[0021] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the intelligent product matching recommendation method described in the first aspect above.

[0022] The embodiment of the present invention provides a method and device for intelligent product matching recommendation. The method comprises the following steps: obtaining an image of a product currently purchased by a user; performing feature extraction on the image of the currently purchased product based on a trained general meaning model to obtain feature attributes of the image of the currently purchased product, and storing the feature attributes of the image of the currently purchased product in a preset retrieval enhancement vector database, wherein the feature attributes include product category, product size, and product style; performing feature analysis on the feature attributes of the image of the currently purchased product based on a feature attribute analysis and matching strategy to obtain matching feature attributes of a product that matches the currently purchased product based on the feature attributes of the image of the currently purchased product; and matching the matching feature attributes of the product that matches the currently purchased product with the product in the preset retrieval enhancement vector database based on a similarity calculation strategy. A similarity matching calculation is performed on the characteristic attributes to obtain a similarity calculation result, and the product characteristic attributes corresponding to the similarity calculation result greater than a preset threshold are filtered out from a preset retrieval enhancement vector database to obtain a first product attribute set; user behavior annotation data is obtained from a preset storage area, and the first product characteristic attribute set is retrieved and filtered based on the user behavior annotation data and the retrieval enhancement model to filter out product characteristic attributes that are close to the user's preferences from the first product characteristic attribute set; based on the product characteristic attributes that are close to the user's preferences, several corresponding products are obtained from the product database, and the several products are matched with the currently purchased products to obtain a matching plan to recommend to the corresponding user.

[0023] In the above method, after extracting and analyzing the features of the purchased product images through a large model, the database is searched for products that match the purchased products and are close to the user's preferences based on the product's characteristic attributes and user preferences, and recommended to the corresponding user. This can not only improve the accuracy of product matching, but also increase the sales conversion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 A flowchart of the intelligent product matching recommendation method provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of a sub-process of the method for intelligent product matching recommendation provided by an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of another sub-process of the method for intelligent product matching recommendation provided by an embodiment of the present invention;

[0028] Figure 4 A schematic block diagram of a device for intelligent product matching recommendation provided by an embodiment of the present invention;

[0029] Figure 5 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0032] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0033] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0034] The embodiment of the present invention provides a method for intelligently recommending product combinations. Figure 1 This is a flow chart of the method for intelligent product matching recommendation provided by an embodiment of the present invention, as shown in FIG. Figure 1 As shown, the method includes steps S110 to S160.

[0035] S110: Obtain images of products currently purchased by the user.

[0036] In this embodiment, an image of a product currently purchased by the user is obtained, for example, an image of a white chiffon long-sleeved top.

[0037] S120. Perform feature extraction on the image of the currently purchased product based on the trained Tongyi model to obtain characteristic attributes of the image of the currently purchased product, and store the characteristic attributes of the image of the currently purchased product in a preset retrieval enhancement vector database, wherein the characteristic attributes include product category, product size, and product style.

[0038] Preferably, before performing feature extraction on the current purchased product image based on the trained Tongyi model to obtain the characteristic attributes of the current purchased product image, it is necessary to first train the Tongyi model. Specifically, obtain the historical product images purchased by the user; then perform similar image matching calculation on the historical product images purchased by the user based on the image matching strategy to obtain extended images similar to the historical product images purchased by the user; finally, train the Tongyi model based on the historical product images purchased by the user and the extended images similar to the historical product images purchased by the user to obtain the trained Tongyi model.

[0039] Next, in this embodiment, based on the above-trained Tongyi model, feature extraction is performed on the image of the currently purchased product to obtain the characteristic attributes of the currently purchased product image, and the characteristic attributes of the currently purchased product image are stored in a preset retrieval enhancement vector database, wherein the characteristic attributes include product category, product size, and product style. For example, based on the above-trained Tongyi model, feature extraction is performed on the image of a white chiffon long-sleeved top. The characteristic attributes of the white chiffon long-sleeved top that can be extracted include: product color is white, product category is women's top, product style is lady style, etc.

[0040] It should be noted that storing the characteristic attributes of the current purchased product image in the preset retrieval enhancement vector database can be used to enrich the characteristic attribute data of the products in the retrieval enhancement vector database, so that when recommending other products for matching, the feature matching similarity rate can be improved to improve the accuracy of product matching.

[0041] S130 , performing feature analysis on the feature attributes of the currently purchased product image based on the feature attribute analysis and matching strategy, so as to obtain matching feature attributes of a product that matches the currently purchased product based on the feature attributes of the currently purchased product image.

[0042] In this embodiment, a feature attribute analysis and matching strategy is used to analyze the feature attributes of the currently purchased product image, so as to obtain matching feature attributes of a product that matches the currently purchased product based on the feature attributes of the currently purchased product image. Specifically, the following steps are performed: based on the trained Tongyi model, the feature attributes of the currently purchased product image are analyzed to obtain key feature attribute information of the currently purchased product image; based on the key feature attribute information, feature attribute expansion and association are performed on the currently purchased product to obtain associated feature attributes; and based on the associated feature attributes and key feature attribute information, matching feature attributes of a product that matches the currently purchased product are obtained. For example, based on the trained Tongyi model, the feature attributes of an image of a white chiffon long-sleeved top, i.e., the product color is white, the product category is women's top, and the product style is ladies' style, are analyzed to obtain key feature attribute information of the image of the white chiffon long-sleeved top as "ladies' white ladies' style top." Then, based on the key feature attribute information, the feature attributes of the currently purchased product are expanded and associated to obtain associated feature attributes. For example, the product is a versatile item that can be matched with any color of pants or skirt. The product is suitable for commuting, formal occasions, or going out on dates. The style of the product is suitable for a slim fit on the lower body. Finally, based on the associated feature attributes and key feature attribute information, matching feature attributes of products that match the white chiffon long-sleeved top are obtained, such as a pink skirt, jeans, etc.

[0043] Preferably, before performing feature analysis on the feature attributes of the current purchased product image based on the trained Tongyi model to obtain the key feature attribute information of the current purchased product image, it is necessary to first train the Tongyi model. Specifically, obtain the historical product images purchased by the user; then perform similar image matching calculations on the historical product images purchased by the user based on the image matching strategy to obtain extended images similar to the historical product images purchased by the user; finally, train the Tongyi model based on the historical product images purchased by the user and the extended images similar to the historical product images purchased by the user to obtain the trained Tongyi model.

[0044] S140. Based on the similarity calculation strategy, a similarity matching calculation is performed on the matching feature attributes of the product that matches the currently purchased product and the feature attributes of the product in the preset retrieval enhancement vector database to obtain a similarity calculation result, and the feature attributes of the product corresponding to the similarity calculation result greater than a preset threshold are filtered out from the preset retrieval enhancement vector database to obtain a first product attribute set.

[0045] In this embodiment, a similarity calculation strategy is used to perform a similarity matching calculation on the matching feature attributes of the product that matches the currently purchased product and the feature attributes of the product in the preset search enhancement vector database to obtain a similarity calculation result. It should be noted that similarity calculation strategies include, but are not limited to, Euclidean distance and angle cosine.

[0046] Next, the similarity calculation result is compared with a preset threshold. If it is greater than the preset threshold, the product feature attributes corresponding to the similarity calculation result greater than the preset threshold are filtered out from the preset retrieval enhancement vector database to obtain a first product attribute set.

[0047] S150. Obtain user behavior annotation data from a preset storage area, and search and filter the first product feature attribute set based on the user behavior annotation data and the retrieval enhancement model to obtain product feature attributes that are close to the user's preferences from the first product feature attribute set.

[0048] In this embodiment, preferably, before obtaining the user behavior annotation data from the preset storage area, the user's historical behavior information is obtained, and the corresponding user is labeled based on the user's historical behavior information to obtain the user behavior annotation data, and the user behavior annotation data is stored in the preset storage area.

[0049] Then, user behavior annotation data is obtained from a preset storage area, and the product feature attributes in the first product feature attribute set are retrieved and filtered based on the user behavior annotation data and the retrieval enhancement model to obtain product feature attributes that are close to the user's preferences from the first product feature attribute set.

[0050] S160: Based on the characteristic attributes of the products that are close to the user's preferences, corresponding products are obtained from the product database, and the products are matched with the currently purchased products to obtain a matching solution to recommend to the corresponding user.

[0051] In this embodiment, based on the product attributes that are close to the user's preferences, a number of corresponding products are searched from the product database, and the obtained products are matched with the currently purchased products one by one to obtain a matching solution to recommend to the corresponding user.

[0052] In one embodiment, if Figure 2 As shown, after a number of commodities are matched with the currently purchased commodities to obtain a matching solution, step S160 includes sub-steps S161, S162 and S163.

[0053] S161. Obtain a matching plan.

[0054] S162: Evaluate and calculate each matching solution in the matching solution based on the custom scoring strategy to obtain an evaluation calculation result.

[0055] S163: Select the best matching solution from the matching solutions based on the evaluation calculation results and recommend it to the corresponding user.

[0056] In one embodiment, if Figure 3 As shown, after a number of commodities are matched with the currently purchased commodities to obtain a matching solution, step S160 further includes sub-steps S1601, S1602 and S1603.

[0057] S1601. Obtain a matching plan.

[0058] S1602: performing image splicing processing on the product images under each matching scheme in the matching scheme based on the image generation strategy to generate an overall matching image effect corresponding to each matching scheme.

[0059] S1603: Evaluate and calculate each matching solution in the matching solution based on the custom scoring strategy to obtain an evaluation calculation result.

[0060] S1604: Based on actual needs, weighted calculation is performed on the overall matching image effects and evaluation calculation results corresponding to each matching scheme to obtain a calculation result, and the best matching scheme is selected from the matching schemes according to the calculation result to be recommended to the corresponding user.

[0061] In one embodiment, each matching scheme in a matching scheme is evaluated and calculated based on a custom scoring strategy to obtain an evaluation result, including: obtaining the matching elements of each matching scheme and the score of each matching element in the matching elements, wherein the matching elements include matching themes, matching colors, product textures, and suitable occasions; in addition, it should be noted that the sum of the scores of each matching element is 100. Then, based on the actual situation, each matching element in each matching scheme is scored, and the scored matching elements are weighted based on the weight calculation strategy to obtain the evaluation result of each matching scheme. For example, the matching elements of each matching scheme and the score of each matching element in the matching elements are obtained, and in addition, it should be noted that the sum of the scores of each matching element is 100. Then, based on the actual situation, each matching element in each matching scheme is scored, for example, the matching theme is scored as 15 points, the matching color is scored as 15 points, the product texture is scored as 12 points, and the suitable occasion is scored as 15 points; then, based on the weight calculation strategy, each matching element is weighted based on the weight calculation strategy to obtain the evaluation result of each matching scheme.

[0062] In the product intelligent matching recommendation method provided by the embodiment of the present invention, the user's currently purchased product image is obtained; based on the trained Tongyi model, feature extraction is performed on the currently purchased product image to obtain feature attributes of the currently purchased product image, and the feature attributes of the currently purchased product image are stored in a preset retrieval enhancement vector database, wherein the feature attributes include product category, product size, and product style; based on the feature attribute analysis and matching strategy, feature analysis is performed on the feature attributes of the currently purchased product image to obtain matching feature attributes of the product that matches the currently purchased product based on the feature attributes of the currently purchased product image; based on the similarity calculation strategy, the matching feature attributes of the product that matches the currently purchased product and the preset retrieval enhancement vector database are compared. A similarity matching calculation is performed on the product feature attributes in the search enhancement vector database to obtain a similarity calculation result, and the product feature attributes corresponding to the similarity calculation result greater than a preset threshold are filtered out from the preset search enhancement vector database to obtain a first product attribute set; user behavior annotation data is obtained from a preset storage area, and the first product feature attribute set is retrieved and filtered based on the user behavior annotation data and the search enhancement model to filter out product feature attributes that are close to the user's preferences from the first product feature attribute set; based on the product feature attributes that are close to the user's preferences, several corresponding products are obtained from the product database, and the several products are matched with the currently purchased products to obtain a matching solution for recommendation to the corresponding user. In the above method, after feature extraction and feature analysis of the purchased product image using a large model, products that are close to the user's preferences and that match the purchased product are searched from the database based on the product feature attributes and user preferences, and are recommended to the corresponding user. This can not only improve the accuracy of product matching, but also increase the sales conversion rate.

[0063] The embodiment of the present invention also provides a device for intelligently recommending products. Figure 4 This is a schematic block diagram of a device for intelligent product matching recommendation provided by an embodiment of the present invention. Figure 4 As shown, the intelligent product combination recommendation device 100 includes an acquisition unit 110 , a feature extraction unit 120 , a feature analysis unit 130 , a screening unit 140 , and a combination recommendation unit 150 .

[0064] The acquisition unit 110 is used to acquire the image of the product currently purchased by the user.

[0065] In this embodiment, an image of a product currently purchased by the user is obtained, for example, an image of a white chiffon long-sleeved top.

[0066] The feature extraction unit 120 is used to extract features of the current purchased product image based on the trained general meaning model, obtain feature attributes of the current purchased product image, and store the feature attributes of the current purchased product image in a preset retrieval enhancement vector database, wherein the feature attributes include product category, product size, and product style.

[0067] Preferably, before performing feature extraction on the current purchased product image based on the trained Tongyi model to obtain the characteristic attributes of the current purchased product image, it is necessary to first train the Tongyi model. Specifically, obtain the historical product images purchased by the user; then perform similar image matching calculation on the historical product images purchased by the user based on the image matching strategy to obtain extended images similar to the historical product images purchased by the user; finally, train the Tongyi model based on the historical product images purchased by the user and the extended images similar to the historical product images purchased by the user to obtain the trained Tongyi model.

[0068] Next, in this embodiment, based on the above-trained Tongyi model, feature extraction is performed on the image of the currently purchased product to obtain the characteristic attributes of the currently purchased product image, and the characteristic attributes of the currently purchased product image are stored in a preset retrieval enhancement vector database, wherein the characteristic attributes include product category, product size, and product style. For example, based on the above-trained Tongyi model, feature extraction is performed on the image of a white chiffon long-sleeved top. The characteristic attributes of the white chiffon long-sleeved top that can be extracted include: product color is white, product category is women's top, product style is lady style, etc.

[0069] It should be noted that storing the characteristic attributes of the current purchased product image in the preset retrieval enhancement vector database can be used to enrich the characteristic attribute data of the products in the retrieval enhancement vector database, so that when recommending other products for matching, the feature matching similarity rate can be improved to improve the accuracy of product matching.

[0070] The feature analysis unit 130 is used to perform feature analysis on the feature attributes of the current purchased product image based on the feature attribute analysis matching strategy, so as to obtain matching feature attributes of the product that matches the current purchased product based on the feature attributes of the current purchased product image.

[0071] In this embodiment, a feature attribute analysis and matching strategy is used to analyze the feature attributes of the currently purchased product image, so as to obtain matching feature attributes of a product that matches the currently purchased product based on the feature attributes of the currently purchased product image. Specifically, the following steps are performed: based on the trained Tongyi model, the feature attributes of the currently purchased product image are analyzed to obtain key feature attribute information of the currently purchased product image; based on the key feature attribute information, feature attribute expansion and association are performed on the currently purchased product to obtain associated feature attributes; and based on the associated feature attributes and key feature attribute information, matching feature attributes of a product that matches the currently purchased product are obtained. For example, based on the trained Tongyi model, the feature attributes of an image of a white chiffon long-sleeved top, i.e., the product color is white, the product category is women's top, and the product style is ladies' style, are analyzed to obtain key feature attribute information of the image of the white chiffon long-sleeved top as "ladies' white ladies' style top." Then, based on the key feature attribute information, the feature attributes of the currently purchased product are expanded and associated to obtain associated feature attributes. For example, the product is a versatile item that can be matched with any color of pants or skirt. The product is suitable for commuting, formal occasions, or going out on dates. The style of the product is suitable for a slim fit on the lower body. Finally, based on the associated feature attributes and key feature attribute information, matching feature attributes of products that match the white chiffon long-sleeved top are obtained, such as a pink skirt, jeans, etc.

[0072] Preferably, before performing feature analysis on the feature attributes of the current purchased product image based on the trained Tongyi model to obtain the key feature attribute information of the current purchased product image, it is necessary to first train the Tongyi model. Specifically, obtain the historical product images purchased by the user; then perform similar image matching calculations on the historical product images purchased by the user based on the image matching strategy to obtain extended images similar to the historical product images purchased by the user; finally, train the Tongyi model based on the historical product images purchased by the user and the extended images similar to the historical product images purchased by the user to obtain the trained Tongyi model.

[0073] The screening unit 140 performs similarity matching calculation on the matching feature attributes of the product that matches the currently purchased product and the feature attributes of the product in the preset retrieval enhancement vector database based on the similarity calculation strategy to obtain a similarity calculation result, and screens the feature attributes of the product corresponding to the similarity calculation result greater than a preset threshold from the preset retrieval enhancement vector database to obtain a first product attribute set, obtains user behavior annotation data from a preset storage area, and searches and screens the first product feature attribute set based on the user behavior annotation data and the retrieval enhancement model to obtain product feature attributes that are close to the user's preferences from the first product feature attribute set.

[0074] In this embodiment, a similarity calculation strategy is used to perform a similarity matching calculation on the matching feature attributes of the product that matches the currently purchased product and the feature attributes of the product in the preset search enhancement vector database to obtain a similarity calculation result. It should be noted that similarity calculation strategies include, but are not limited to, Euclidean distance and angle cosine.

[0075] Next, the similarity calculation result is compared with a preset threshold. If it is greater than the preset threshold, the product feature attributes corresponding to the similarity calculation result greater than the preset threshold are filtered out from the preset retrieval enhancement vector database to obtain a first product attribute set.

[0076] Then, user behavior annotation data is obtained from a preset storage area, and the product feature attributes in the first product feature attribute set are retrieved and filtered based on the user behavior annotation data and the retrieval enhancement model to obtain product feature attributes that are close to the user's preferences from the first product feature attribute set.

[0077] Preferably, before obtaining the user behavior annotation data from the preset storage area, the user's historical behavior information is obtained, and the corresponding user is labeled based on the user's historical behavior information to obtain the user behavior annotation data, and the user behavior annotation data is stored in the preset storage area.

[0078] The combination recommendation unit 150 is used to obtain corresponding products from the product database based on product feature attributes that are close to the user's preferences, and combine the products with the currently purchased products to obtain a combination plan to recommend to the corresponding user.

[0079] In this embodiment, based on the product attributes that are close to the user's preferences, a number of corresponding products are searched from the product database, and the obtained products are matched with the currently purchased products one by one to obtain a matching solution to recommend to the corresponding user.

[0080] In one embodiment, after a combination of several commodities and currently purchased commodities is obtained, the combination recommendation unit 150 includes the following.

[0081] Get matching plans.

[0082] Based on the custom scoring strategy, each matching scheme in the matching scheme is evaluated and calculated to obtain an evaluation calculation result.

[0083] Based on the evaluation calculation results, the best matching scheme is selected from the matching schemes to be recommended to the corresponding user.

[0084] In one embodiment, after a combination of several commodities and the currently purchased commodity is obtained, the combination recommendation unit 150 further includes the following.

[0085] Get matching plans.

[0086] Based on the image generation strategy, the product images under each matching scheme in the matching scheme are spliced ​​to generate the overall matching image effect corresponding to each matching scheme.

[0087] Based on the custom scoring strategy, each matching scheme in the matching scheme is evaluated and calculated to obtain an evaluation calculation result.

[0088] S1604: Based on actual needs, weighted calculation is performed on the overall matching image effects and evaluation calculation results corresponding to each matching scheme to obtain a calculation result, and the best matching scheme is selected from the matching schemes according to the calculation result to be recommended to the corresponding user.

[0089] In one embodiment, each matching scheme in a matching scheme is evaluated and calculated based on a custom scoring strategy to obtain an evaluation result, including: obtaining the matching elements of each matching scheme and the score of each matching element in the matching elements, wherein the matching elements include matching themes, matching colors, product textures, and suitable occasions; in addition, it should be noted that the sum of the scores of each matching element is 100. Then, based on the actual situation, each matching element in each matching scheme is scored, and the scored matching elements are weighted based on the weight calculation strategy to obtain the evaluation result of each matching scheme. For example, the matching elements of each matching scheme and the score of each matching element in the matching elements are obtained, and in addition, it should be noted that the sum of the scores of each matching element is 100. Then, based on the actual situation, each matching element in each matching scheme is scored, for example, the matching theme is scored as 15 points, the matching color is scored as 15 points, the product texture is scored as 12 points, and the suitable occasion is scored as 15 points; then, based on the weight calculation strategy, each matching element is weighted based on the weight calculation strategy to obtain the evaluation result of each matching scheme.

[0090] The intelligent product matching recommendation device provided in the embodiment of the present invention applies the above-mentioned intelligent product matching recommendation method to obtain the user's currently purchased product image; based on the trained Tongyi model, feature extraction is performed on the currently purchased product image to obtain the feature attributes of the currently purchased product image, and the feature attributes of the currently purchased product image are stored in a preset retrieval enhancement vector database, wherein the feature attributes include product category, product size, and product style; based on the feature attribute analysis matching strategy, feature analysis is performed on the feature attributes of the currently purchased product image to obtain matching feature attributes of the product that matches the currently purchased product according to the feature attributes of the currently purchased product image; based on the similarity calculation strategy, the matching feature attributes of the product that matches the currently purchased product are calculated. The method performs a similarity matching calculation on the product feature attributes in a preset retrieval enhancement vector database, obtains a similarity calculation result, and filters the product feature attributes corresponding to the similarity calculation result greater than a preset threshold from the preset retrieval enhancement vector database to obtain a first product attribute set; obtains user behavior annotation data from a preset storage area, and searches and filters the first product feature attribute set based on the user behavior annotation data and the retrieval enhancement model to filter out product feature attributes that are close to the user's preferences from the first product feature attribute set; obtains several corresponding products from the product database based on the product feature attributes that are close to the user's preferences, and matches the several products with the currently purchased products to obtain a matching solution for recommendation to the corresponding user. In the above method, after extracting and analyzing the features of the purchased product images using a large model, the method searches the database for products that are close to the user's preferences based on the product feature attributes and user preferences, and recommends them to the corresponding user. This method not only improves the accuracy of product matching, but also increases the sales conversion rate.

[0091] The above-mentioned intelligent product matching recommendation method can be implemented in the form of a computer program. The computer program can be used in Figure 5 Runs on the computer equipment shown.

[0092] See also Figure 5 , Figure 5 1 is a schematic block diagram of a computer device provided in an embodiment of the present invention. The computer device may be used to execute a method for intelligent product matching recommendation.

[0093] See Figure 5 The computer device 500 includes a processor 502 , a memory, and a network interface 505 connected via a system bus 501 , wherein the memory may include a storage medium 503 and an internal memory 504 .

[0094] The storage medium 503 can store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, the processor 502 can execute the product intelligent matching recommendation method. The storage medium 503 can be a volatile storage medium or a non-volatile storage medium.

[0095] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500.

[0096] The internal memory 504 provides an environment for the operation of the computer program 5032 in the storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute the product intelligent matching recommendation method.

[0097] The network interface 505 is used for network communication, such as providing data information transmission. Those skilled in the art will understand that Figure 5 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device 500 to which the solution of the present invention is applied. The specific computer device 500 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0098] The processor 502 is configured to execute a computer program 5032 stored in the memory to implement the corresponding functions of the above-mentioned intelligent product matching recommendation method.

[0099] Those skilled in the art will understand that Figure 5 The embodiment of the computer device shown in the figure does not constitute a limitation on the specific composition of the computer device. In other embodiments, the computer device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, in some embodiments, the computer device may only include a memory and a processor. In such an embodiment, the structure and function of the memory and processor are the same as those in the figure. Figure 5 The embodiments shown are consistent and will not be described again here.

[0100] It should be understood that in the embodiment of the present invention, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0101] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium may be volatile or non-volatile. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps included in the aforementioned intelligent product pairing recommendation method.

[0102] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0103] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, or units with the same function may be combined into one unit. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices or units, or may be an electrical, mechanical or other form of connection.

[0104] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the objectives of the embodiments of the present invention.

[0105] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0106] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a computer-readable storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned computer-readable storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk.

[0107] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for intelligent product matching recommendation, characterized in that: The method comprises: Get the image of the product currently purchased by the user; Extracting features of the currently purchased product image based on the trained Tongyi model to obtain characteristic attributes of the currently purchased product image, and storing the characteristic attributes of the currently purchased product image in a preset retrieval enhancement vector database, wherein the characteristic attributes include product category, product size, and product style; Performing feature analysis on the feature attributes of the currently purchased product image based on the feature attribute analysis and matching strategy to obtain matching feature attributes of a product that matches the currently purchased product based on the feature attributes of the currently purchased product image; performing a similarity matching calculation on the matching characteristic attributes of the product that matches the currently purchased product and the product characteristic attributes in the preset search enhancement vector database based on a similarity calculation strategy to obtain a similarity calculation result, and filtering out product characteristic attributes corresponding to the similarity calculation result that is greater than a preset threshold from the preset search enhancement vector database to obtain a first product attribute set; Obtaining user behavior annotation data from a preset storage area, and searching and screening the first set of product feature attributes based on the user behavior annotation data and a search enhancement model to obtain product feature attributes that are close to the user's preferences from the first set of product feature attributes; Based on the characteristic attributes of the commodities that are close to the user's preferences, a number of corresponding commodities are obtained from the commodity database, and the number of commodities are matched with the currently purchased commodities to obtain a matching plan to recommend to the corresponding user.

2. The method according to claim 1, characterized in that Before acquiring the user behavior annotation data from the preset storage area, the method includes: Obtain user historical behavior information, and set tags for corresponding users based on the user historical behavior information to obtain user behavior annotation data, and store the user behavior annotation data in the preset storage area.

3. The method according to claim 1, characterized in that The feature attribute analysis and matching strategy based on the feature attribute analysis is performed on the feature attributes of the currently purchased product image to obtain matching feature attributes of a product that matches the currently purchased product based on the feature attributes of the currently purchased product image, including: Performing feature analysis on the feature attributes of the currently purchased product image based on the trained Tongyi model to obtain key feature attribute information of the currently purchased product image, and performing feature attribute expansion association on the currently purchased product based on the key feature attribute information to obtain associated feature attributes; The matching characteristic attributes of the commodity that matches the currently purchased commodity are obtained based on the associated characteristic attributes and the key characteristic attribute information.

4. The method according to claim 1 or 3, characterized in that Before extracting features from the current purchased product image based on the trained Tongyi model to obtain characteristic attributes of the current purchased product image, and performing feature analysis on the characteristic attributes of the current purchased product image based on the trained Tongyi model to obtain key characteristic attribute information of the current purchased product image, the method includes: Get the historical product images purchased by the user; Performing similar image matching calculation on the historical product images purchased by the user based on an image matching strategy to obtain an expanded image similar to the historical product images purchased by the user; The Tongyida model is trained based on the historical product images purchased by the user and the expanded images similar to the historical product images purchased by the user to obtain the trained Tongyida model.

5. The method according to claim 1, wherein After the plurality of commodities are matched with the currently purchased commodities to obtain a matching solution, the method includes: Obtaining the matching solution; Evaluate and calculate each matching scheme in the matching scheme based on a custom scoring strategy to obtain an evaluation calculation result; The best matching scheme is selected from the matching schemes based on the evaluation calculation result to be recommended to the corresponding user.

6. The method according to claim 1, wherein After the plurality of commodities are matched with the currently purchased commodities to obtain a matching solution, the method further includes: Obtaining the matching solution; Based on the image generation strategy, image splicing processing is performed on the product images under each matching scheme in the matching scheme to generate an overall matching image effect corresponding to each matching scheme; Evaluate and calculate each matching scheme in the matching scheme based on a custom scoring strategy to obtain an evaluation calculation result; Based on actual needs, a weighted calculation is performed on the overall matching image effects corresponding to each matching scheme and the evaluation calculation results to obtain a calculation result, and the best matching scheme is selected from the matching schemes according to the calculation result to be recommended to the corresponding user.

7. The method according to claim 5 or 6, characterized in that The step of evaluating and calculating each of the collocation schemes based on the custom scoring strategy to obtain an evaluation and calculation result includes: Obtaining the matching elements of each matching scheme and the score of each matching element in the matching elements, wherein the matching elements include matching theme, matching color, product texture, and suitable occasion; Based on the actual situation, each matching element in each matching scheme is scored, and based on the weight calculation strategy, each scoring matching element is weighted to obtain the evaluation calculation results of each matching scheme.

8. A device for intelligent product matching recommendation, characterized in that: The device comprises: An acquisition unit, used to acquire images of products currently purchased by the user; a feature extraction unit, configured to extract features from the currently purchased product image based on the trained general meaning model, obtain feature attributes of the currently purchased product image, and store the feature attributes of the currently purchased product image in a preset retrieval enhancement vector database, wherein the feature attributes include product category, product size, and product style; a feature analysis unit configured to perform feature analysis on the feature attributes of the currently purchased product image based on a feature attribute analysis and matching strategy, so as to obtain matching feature attributes of a product that matches the currently purchased product based on the feature attributes of the currently purchased product image; a screening unit configured to perform similarity matching calculation on the matching characteristic attributes of the product that matches the currently purchased product and the product characteristic attributes in the preset search enhancement vector database based on a similarity calculation strategy, obtain a similarity calculation result, and screen out product characteristic attributes corresponding to the similarity calculation result that is greater than a preset threshold from the preset search enhancement vector database to obtain a first product attribute set; Obtaining user behavior annotation data from a preset storage area, and searching and screening the first set of product feature attributes based on the user behavior annotation data and a search enhancement model to obtain product feature attributes that are close to the user's preferences from the first set of product feature attributes; The combination recommendation unit is used to obtain corresponding products from the product database based on the product feature attributes that are close to the user's preferences, and to match the products with the currently purchased products to obtain a combination plan to recommend to the corresponding user.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for intelligent product matching recommendation according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for intelligent product matching recommendation according to any one of claims 1 to 7 is implemented.

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