Product recommendation methods based on big data inference and fine-grained user demand analysis

Abstract

This invention relates to a product recommendation method based on big data inference and fine-grained user demand analysis, comprising: constructing a target user attribute vector; obtaining a first product and a second product based on the products in which the user's behavior occurred within a first predetermined time period; filtering target products from the second product; determining a first recommendation factor, a second recommendation factor, and a comprehensive recommendation factor for the target product; and recommending the target product based on the comprehensive recommendation factor. This invention constructs unique user attribute information for each product, integrating user attributes related to the product while also considering the behavioral data of a group of users who have engaged in consumption-related behaviors related to the product. By fusing the behavioral data of the group of users with the user attributes behind the product, dynamic, multi-dimensional user attribute information is obtained, enabling more targeted, accurate, and fine-grained product recommendations tailored to the user's specific needs.

Description

Technical Field

[0001] This invention relates to the field of big data personalized information recommendation technology, specifically to a product recommendation method based on big data reasoning and fine-grained user demand analysis. Background Technology

[0002] The internet and smart mobile devices have greatly facilitated people's lives. As online shopping and information retrieval have permeated people's daily lives and work, people are exposed to various recommendation services provided by search engines.

[0003] Internet recommendation functions are frequently used and constantly updated technologies. Existing recommendation functions either recommend based on user preferences or on group user ratings of products. However, to date, no technology has been found that integrates the behavior of group users into the user attributes behind the products to achieve information recommendation based on user attributes.

[0004] In addition, existing technologies recommend products based on user ratings, which can easily lead to a "cold start" problem for newly launched products. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a product recommendation method based on big data reasoning and fine-grained user demand analysis, comprising the following steps:

[0006] Step S1: Construct the target user attribute vector based on the user's attribute information;

[0007] Step S2: Based on the products associated with the user's behavior within the first predetermined time period, construct a big data inference ladder table for the target user. This ladder table includes three levels of inference logic:

[0008] The first step refers to the first user attribute vector and the first behavior dimension of the first product for which the user's behavior occurred within the first predetermined time period;

[0009] The second tier refers to the recommended users who have engaged in an activity related to the first item in the first tier within a first predetermined time period;

[0010] The three-tiered ladder points to the second user attribute vector and the second behavior dimension of the second product recommended by the user during the first predetermined time period.

[0011] Step S3: Select the target product from the second product list;

[0012] Based on the second user attribute vector of the target product and the first user attribute vector of the first product, determine the first recommendation factor of the target product;

[0013] Based on the target user attribute vector and the target product's second user attribute vector, determine the target product's second recommendation factor;

[0014] Based on the first and second recommendation factors, the comprehensive recommendation factor for the target product is determined.

[0015] Step S4: Recommend target products based on comprehensive recommendation factors.

[0016] In step S1, based on the user's attribute information, the vectors of each attribute are determined, and the vectors of each attribute are concatenated to obtain the target user attribute vector. The user's attribute information is attribute information related to the user's needs, including age, gender, income range, target product categories, and consumption volume range in the past year.

[0017] In step S2, the method for determining the dimension of the first line of the first commodity is as follows:

[0018] = / n;

[0019] Wherein, the superscript j represents the specific first product, As the first commodity The first line represents the dimension, where n is the value for the first commodity. The number of users in the group where the behavior occurred. For the first A group of users' views on the first product The absolute value of the behavior vector in which the behavior occurred;

[0020] The method for determining the first user attribute vector is as follows:

[0021] = / ;

[0022] in, Let j be the first user attribute vector of the first product j. For the first product The first time the behavior occurred User attribute vectors for a group of users;

[0023] The method for determining the dimension of the second aspect of the second commodity is as follows:

[0024] = / m;

[0025] Wherein, the superscript k indicates a specific second product, For the second commodity The second line represents the dimension of the second commodity, where m is the unit of measurement for the second commodity. The number of users in the group where the behavior occurred. For the first A group of users on the second product The absolute value of the behavior vector in which the behavior occurred;

[0026] The method for determining the second user attribute vector is as follows:

[0027] = / ;

[0028] in, Let k be the second user attribute vector of the second product. For the action that occurs in response to the second commodity k User attribute vectors for each group of users.

[0029] In step S3, the method for selecting target products includes: identifying products that belong to the second category but not to the first category as target products.

[0030] In step S3, the similarity between the second user attribute vector of the target product and the first user attribute vector of each first product is determined, and the highest similarity is taken as the first recommendation factor of the target product.

[0031] In step S3, the first product with the highest similarity adopted when determining the first recommendation factor of the target product is determined as the first target product. Based on the first behavioral dimension of the first target product and the second behavioral dimension of the target product, the weights of the first recommendation factor and the second recommendation factor are determined. Then, based on the first recommendation factor, the second recommendation factor, and the weights of the first recommendation factor and the second recommendation factor, the comprehensive recommendation factor of the target product is determined.

[0032] In step S3, the similarity between the second user attribute vector of the target product and the target user attribute vector is determined as the second recommendation factor of the target product.

[0033] This includes step S5: taking newly released products within the second predetermined time period, and selecting some products from the newly released products for recommendation based on the similarity between the product attribute vector of the newly released products and the product attribute vector of the target products recommended in step S4.

[0034] This invention constructs unique user attribute information for each product. The user attribute information integrates user attributes that are highly relevant to the product, such as gender, age, and hobbies. At the same time, it also takes into account the behavioral data of the group of users who have made consumption-related behaviors for the product. By integrating the behavioral data of the group of users with the user attributes behind the product, dynamic and multi-dimensional user attribute information is obtained, which enables more targeted and accurate product recommendations. Attached Figure Description

[0035] Figure 1 This is a mind map illustrating the product recommendation method based on big data reasoning and fine-grained user demand analysis of the present invention.

[0036] Figure 2 This is a reasoning path diagram of a big data reasoning ladder table for a target user in one embodiment of the present invention. Detailed Implementation

[0037] To gain a better understanding of the technical solution and beneficial effects of the present invention, the technical solution of the present invention and its beneficial effects are described in detail below with reference to the accompanying drawings.

[0038] Existing technologies for product recommendation either consider only user preferences, age, or product ratings, or even when considering both, they merely separate these two aspects within the same recommendation technology, resulting in sequential product selection. This invention aims to construct a method that builds unique user attribute information for each product. This user attribute information integrates highly relevant user attributes such as gender, age, and preferences of users associated with the product. Simultaneously, it considers behavioral data from groups of users who have made purchase-related decisions regarding the product. By merging this behavioral data with the underlying user attributes, dynamic, multi-dimensional user attribute information is obtained, enabling more targeted, accurate, and granular product recommendations tailored to users' specific needs.

[0039] Combination Figure 1 As shown, the product recommendation method based on big data reasoning and fine-grained user demand analysis provided by the present invention includes the following steps S1 to S5.

[0040] Step S1: Determine the target user attribute vector.

[0041] The target user can be understood as a user who needs product recommendations. This invention determines the user's attribute information, determines its attribute vector based on each attribute information, and concatenates the attribute vectors to obtain the target user attribute vector.

[0042] User attribute information may include key information that affects their choice of goods and consumption, such as age, gender, income range, target product categories, and consumption volume range in the past year. The values ​​of these information are not continuous and have a wide range. This invention constructs an 8-dimensional sparse vector through read-hot encoding to map the attribute information one by one into the attribute vector. When the user's attribute corresponds to the type or range corresponding to the vector dimension, the corresponding dimension of the vector takes a value of 1; otherwise, it is 0.

[0043] Taking clothing recommendations as an example, as shown in Table 1: Target User 1 attribute information: female, 18 years old, monthly income of 0, clothing style leans towards casual and sporty, clothing consumption in the past year is less than 5000; Target User 2 attribute information: female, 35 years old, monthly income of 15000-25000, clothing style leans towards business and professional, clothing consumption in the past year is 5000-15000; Target User 3 attribute information: male, 45 years old, monthly income of more than 25000, clothing style leans towards business and professional, clothing consumption in the past year is 5000-15000.

[0044] Table 1: Target User Attribute Information

[0045]

[0046] For age, gender, monthly income range, audience category, and consumption volume range in the past year, the attribute vectors of target user 1 are (0, 1, 0, 0), (0, 1), (1, 0, 0, 0), (1, 0, 0, 0), and (1, 0, 0, 0). Concatenating these attribute vectors yields the target user attribute vector of target user 1: (0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0). The method for obtaining the target user attribute vectors of target user 2 and target user 3 is similar and will not be repeated here.

[0047] Step S2: Construct a big data inference ladder table for the target users

[0048] like Figure 2 The diagram shows the reasoning path of the big data reasoning ladder table for target users. Assuming that the target users have engaged in activities related to products A, B, and C within a first predetermined time period (such as one week or one month), then recommending users 4, 5, 6, 7, and 8 can be found by identifying users who have engaged in activities related to products A, B, and C within the first predetermined time period. Furthermore, by identifying the products that recommended users 4, 5, 6, 7, and 8 have engaged in activities related to products A, B, C, D, E, F, G, and H within the first predetermined time period, the following products can be identified:

[0049] Thus, the big data inference ladder table for target users includes three levels of inference logic:

[0050] The first level includes: the first set of goods, namely the first user attribute vector and the first behavioral dimension of each of goods A, goods B and goods C;

[0051] The second tier includes: Referral User 4, Referral User 5, Referral User 6, Referral User 7, and Referral User 8;

[0052] The three-tiered ladder includes: the second set of goods, namely the second user attribute vector and the second row dimension of each of goods A, B, C, D, E, F, G and H; goods A, B and C are the overlapping goods in the first set of goods and the second set of goods, and their first user attribute vector and second user attribute vector, as well as the first row dimension and the second row dimension are the same.

[0053] The behaviors of target users or recommended users towards the product within the first predetermined time period include behaviors that represent positive feedback information, such as browsing, favorites, purchases, and sharing.

[0054] The methods for determining the dimension of the first line of the first commodity include:

[0055] = / n; formula (1)

[0056] Wherein, the superscript j represents the specific first product, As the first commodity The first line represents the dimension, where n is the value for the first commodity. The number of users in the group where the behavior occurred. For the first A group of users' views on the first product The absolute value of the behavior vector that caused the behavior; the behavior vector is determined by mapping the specific behavior of a group of users toward the first product to an 8-dimensional sparse vector;

[0057] The methods for determining the first user attribute vector include:

[0058] = / ;Formula (2)

[0059] in, Let j be the first user attribute vector of the first product j. For the first product The first time the behavior occurred The user attribute vector of a group of users; the method for determining the user attribute vector of a group of users is similar to the method for determining the attribute vector of the target user, and will not be repeated here; the group of users are the users who have taken actions on the corresponding product since the product was released.

[0060] Taking product A as an example (where j=A), we assume that the groups of users who have interacted with it, the specific behaviors of each group of users, and the user attribute vectors of each group of users are as shown in Table 2.

[0061] Table 2: Specific behaviors and user attribute vectors of group users

[0062]

[0063] Using browsing, favorites, purchases, and sharing as the order of the behavioral attribute matrix, and employing the same method as for determining the attribute vectors in step S1, the specific behaviors of group users towards product A are mapped to specific behavioral vectors. The behavioral vectors for group users 4, 5, 9, and 10 are obtained as follows: (1, 0, 0, 0), (1, 1, 1, 1), (1, 0, 0, 1), and (1, 1, 0, 0), respectively. Furthermore, the absolute values ​​of the behavioral vectors for group users 4, 5, 9, and 10 are obtained as follows:

[0064] = =1,

[0065] = =2,

[0066] = =1.41,

[0067] = =1.41.

[0068] Based on formulas (1) and (2), the first row of product A has its dimension and the first user attribute vector, respectively, as follows:

[0069] = (1 + 2 + 1.41 + 1.41) / 4 = 1.455;

[0070] =(1 +2 +1.41 +1.41 ) / (1+2+1.41+1.41)=(0, 0.515, 0, 0.485, 0.242, 0.758, 1, 0, 0, 0, 0.414, 0.586, 0, 0, 0.930, 0, 0, 0).

[0071] The method for determining the second dimension of the second product and the second user attribute vector is similar to the method for determining the first dimension of the first product and the first user attribute vector, and will not be repeated here.

[0072] Thus, this invention maps a series of behaviors related to a group of users' consumption of goods into behavior vectors, and then weights the absolute value of the behavior vectors with the user attribute vectors of the group of users. The resulting user attribute vectors can represent both the comprehensive attribute information of the group of users behind the product and the comprehensive behavioral information of the group of users' consumption desire for the product. In this way, when recommending products based on the user attribute information of the product, it can ensure both the fit with the user attributes and the audience reach of the product, thereby ensuring a more accurate recommendation effect.

[0073] Step S3: Filter target products and determine comprehensive recommendation factors

[0074] Step S31: Filter target products: Identify products that belong to the second product category but not to the first product category as target products.

[0075] In this embodiment, the target products (products D, E, F, G, and H) are obtained by subtracting the first product set (products A, B, and C) from the second product set (products A, B, and C).

[0076] Step S32: Determine the primary recommendation factor for the target product.

[0077] In a preferred embodiment of the present invention, the similarity between the second user attribute vector of the target product and the first user attribute vector of each first product is determined, and the highest similarity is taken as the first recommendation factor for the target product.

[0078] max( sim ( ));

[0079] in, The first recommendation factor for the target product q. Represents the first set of goods. sim( represents the second user attribute vector of the target product q) ) represents the second user attribute vector of the target product q. The first user attribute vector of the first product j The similarity.

[0080] For example, for product D (where q=D), calculate the similarity between its second user attribute vector and the first user attribute vectors of products A, B, and C in the first product group. Assuming that the second user attribute vector of product D has the highest similarity to the first user attribute vector of product A, then take this maximum value as the first recommendation factor for product D. .

[0081] Since the user attribute vector of a product is determined by the attributes of the user group behind it, in another preferred embodiment of the present invention, after determining the similarity between the second user attribute vector of the target product and the first user attribute vector of each first product, the overlap of the user groups between the target product and the first products is quantified into an influence factor to correct the similarity. The highest value among the corrected values ​​is taken as the first recommendation factor of the target product, so that the first recommendation factor can more accurately reflect the correlation between the user attributes behind the two products.

[0082] The method for determining the impact factor is as follows:

[0083] = ;

[0084] in, Let be the influencing factor between the target product q and the first product j. This represents the set of users who have interacted with the first item j. This represents the set of users who have interacted with the target product q. Represents a group of users and group user collection The number of users involved in the intersection. Each represents a group of users. and group user collection The number of users involved.

[0085] Continuing with the example above, if the target product is product D (where q=D) and the first product is product A (where j=A), and the user group for product A includes users 4, 5, 9, and 10, and the user group for product D includes users 7, 9, 10, 11, and 12, then when determining the similarity between product D and product A, the influencing factor... =2 2 / (4+5) = 0.44.

[0086] Next, the similarity between this influence factor and product D and product A is multiplied to obtain the corrected value; the maximum value of the corrected values ​​of product D and each of the first products (products A, B, and C) is taken as the first recommendation factor, i.e.:

[0087] max( sim ( ) ).

[0088] Step S33: Determine the second recommendation factor for the target product: Calculate the similarity between the second user attribute vector of the target product and the target user attribute vector, and use this as the second recommendation factor for the target product. .

[0089] Step S34: Select the first recommendation factor With the second recommendation factor The weighted summaries yield the overall recommendation factor. .

[0090] = + ;

[0091] in, The first recommendation factor is respectively and the second recommendation factor The weight.

[0092] In this invention, to more fully reflect the recommendation effect of the user attributes behind the product, an innovative scheme of dynamic weight allocation based on the user attribute vector of the product is adopted:

[0093] (1) First, determine the first recommendation factor for the target product q. At that time, obtain the sim (which makes the target product q) ) The commodity with the highest value is designated as the primary commodity of the target. For ease of distinction, it will be referred to as j in the following text. Refers to the first commodity in the target;

[0094] (2) Determine the first commodity j of the target The first line is the dimension The second line of the target product q is a dimension. ;

[0095] (3) Calculate the dimensions of the second row. The first line has the same dimensions. ratio:

[0096] b= ;

[0097] (3) -1: If this value is greater than 1, it at least indicates that the target product q is relative to the first target product j. Among their respective user groups, each product is more popular; that is, users with similar attributes are more willing to buy the target product q. Therefore, the primary recommendation factor should be... With the second recommendation factor The larger of the two values ​​is assigned a higher weight to maximize the overall recommendation factor value of the target product q. Specifically, the first and second weights are determined based on the ratio b as follows: b / (1+b) and 1 / (1+b).

[0098] If the first recommendation factor Greater than the second recommendation factor Then the first recommendation factor weight = b / (1+b), Second Recommendation Factor weight =1 / (1+b)

[0099] Conversely, if the first recommendation factor Less than the second recommendation factor Then the first recommendation factor weight =1 / (1+b), Second Recommendation Factor weight =b / (1+b)

[0100] (3)-2: Similar logic, if the value of b is less than 1, it at least indicates that the target product q is relative to the first target product j. Among their respective user groups, they are less popular; that is, users with similar attributes are more willing to buy the first item j. The first commodity j However, if the target user has already viewed the product, then the primary recommendation factor should be... With the second recommendation factor The larger of the two values ​​is assigned a lower weight, so that the value of the comprehensive recommendation factor of the target product q is adjusted to be lower. This can be achieved by the same method as above, that is: the first weight and the second weight are determined based on the ratio b as b / (1+b) and 1 / (1+b), respectively.

[0101] If the first recommendation factor Greater than the second recommendation factor Then the first recommendation factor weight = b / (1+b), Second Recommendation Factor weight =1 / (1+b)

[0102] Conversely, if the first recommendation factor Less than the second recommendation factor Then the first recommendation factor weight =1 / (1+b), Second Recommendation Factor weight =b / (1+b)

[0103] Step S4: Sort the target products in descending order based on the comprehensive recommendation factor, and give priority to recommending products with larger comprehensive recommendation factors.

[0104] Step S5: Take newly released products within the second predetermined time period. Based on the similarity of the product attribute vectors of the newly released products and the target products recommended in Step S4, select some products from the newly released products for recommendation to solve the problem of "cold start" of newly released products.

[0105] The method for determining the product attribute vectors of newly released products and recommended target products is similar to the method for determining the target user attribute vectors: by using read-hot encoding, the product attributes (such as type, color, size, price, etc.) are mapped to multiple attribute vectors, and then the multiple attribute vectors are concatenated to obtain the product attribute vector. In addition, this step is to solve the cold start problem of newly released products, so the second reservation time should be significantly shorter than the first reservation time. The second reservation time is set according to the update cycle of the recommended products. For example, clothing products can be set to 1 day, books can be set to 15 days, and electronic digital products can be set to 1 month. The specific values ​​can be set according to experience values, and this invention does not impose any restrictions on them.

[0106] Although the present invention has been described using the above preferred embodiments, it is not intended to limit the scope of protection of the present invention. Any changes and modifications made by those skilled in the art to the above embodiments without departing from the spirit and scope of the present invention shall still fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be defined by the claims.

Claims

1. A product recommendation method based on big data reasoning and fine-grained user demand analysis, characterized in that: Includes the following steps: Step S1: Construct the target user attribute vector based on the user's attribute information; Step S2: Based on the products associated with the user's behavior within the first predetermined time period, construct a big data inference ladder table for the target user. This ladder table includes three levels of inference logic: The first step refers to the first user attribute vector and the first behavior dimension of the first product for which the user's behavior occurred within the first predetermined time period; The second tier refers to the recommended users who have engaged in an activity related to the first item in the first tier within a first predetermined time period; The three-tiered ladder points to the second user attribute vector and the second behavior dimension of the second product recommended by the user during the first predetermined time period. Step S3: Select the target product from the second product list; Based on the second user attribute vector of the target product and the first user attribute vector of the first product, determine the first recommendation factor of the target product; Based on the target user attribute vector and the target product's second user attribute vector, determine the target product's second recommendation factor; Based on the first and second recommendation factors, the comprehensive recommendation factor for the target product is determined. Step S4: Recommend target products based on comprehensive recommendation factors; In step S2, the method for determining the dimension of the first line of the first commodity is as follows: ； Among them, superscript Indicates a specific first commodity. As the first commodity The first line represents the dimension, where n is the value for the first commodity. The number of users in the group where the behavior occurred. For the first A group of users' views on the first product The absolute value of the behavior vector in which the behavior occurred; The method for determining the first user attribute vector is as follows: ； in, As the first commodity The first user attribute vector, For the first product The first time the behavior occurred User attribute vectors for a group of users; The method for determining the dimension of the second aspect of the second commodity is as follows: ； Among them, superscript Indicates a specific second product. For the second commodity The second line represents the dimension of the second commodity, where m is the unit of measurement for the second commodity. The number of users in the group where the behavior occurred. For the first A group of users on the second product The absolute value of the behavior vector in which the behavior occurred; The method for determining the second user attribute vector is as follows: ； in, For the second commodity The second user attribute vector, For the action that occurs in response to the second commodity k User attribute vectors for each group of users.

2. The product recommendation method based on big data reasoning and fine-grained user demand analysis as described in claim 1, characterized in that: In step S1, based on the user's attribute information, the vectors of each attribute are determined, and the vectors of each attribute are concatenated to obtain the target user attribute vector; the user's attribute information is attribute information related to the user's needs, including age, gender, income range, target product categories, and consumption volume range in the past year.

3. The product recommendation method based on big data reasoning and fine-grained user demand analysis as described in claim 1, characterized in that: In step S3, the method for selecting target products includes: identifying products that belong to the second product category but not to the first product category as target products.

4. The product recommendation method based on big data reasoning and fine-grained user demand analysis as described in claim 3, characterized in that: In step S3, the similarity between the second user attribute vector of the target product and the first user attribute vector of each first product is determined, and the highest similarity is taken as the first recommendation factor of the target product.

5. The product recommendation method based on big data reasoning and fine-grained user demand analysis as described in claim 4, characterized in that: In step S3, the first product corresponding to the highest similarity adopted when determining the first recommendation factor of the target product is determined as the first target product. Based on the first row dimension of the first target product and the second row dimension of the target product, the weights of the first recommendation factor and the second recommendation factor are determined. Then, based on the first recommendation factor, the second recommendation factor, and the weights of the first recommendation factor and the second recommendation factor, the comprehensive recommendation factor of the target product is determined.

6. The product recommendation method based on big data reasoning and fine-grained user demand analysis as described in claim 3, characterized in that: In step S3, the similarity between the second user attribute vector of the target product and the target user attribute vector is determined as the second recommendation factor of the target product.

7. The product recommendation method based on big data reasoning and fine-grained user demand analysis as described in claim 1, characterized in that: It also includes step S5: taking newly released products within the second predetermined time period, and selecting some products from the newly released products for recommendation based on the similarity between the product attribute vector of the newly released products and the product attribute vector of the target products recommended in step S4.

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