CN107016589B - Method and device for determining recommended product - Google Patents

Method and device for determining recommended product Download PDF

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CN107016589B
CN107016589B CN201610652089.9A CN201610652089A CN107016589B CN 107016589 B CN107016589 B CN 107016589B CN 201610652089 A CN201610652089 A CN 201610652089A CN 107016589 B CN107016589 B CN 107016589B
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recommended
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刘正
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Advanced New Technologies Co Ltd
Advantageous New Technologies Co Ltd
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Abstract

The application provides a method and a device for determining recommended products, and belongs to the technical field of information processing. The method comprises the following steps: determining product categories in which the target user is interested; acquiring neighbor users interested in the product categories interested in the target user; and determining the recommended products recommended to the target user according to the interest degrees of the neighbor users to the products to be recommended. In the application, the neighbor users and the target user are interested in the same product category, the recommended products determined according to the preferences of the neighbor users are products in the product category interested by the target user, compared with the prior art, the neighbor users and the target user are related to the preferences of all products, and the recommendation accuracy is improved as the recommended products determined by the neighbor users may not belong to the product category interested by the target user.

Description

Method and device for determining recommended product
Technical Field
The present application relates to the field of information processing technologies, and in particular, to a method and an apparatus for determining a recommended product.
Background
With the advent of the network era, electronic commerce has been rapidly developed, and products provided by electronic commerce operators have rapidly increased. How to efficiently and accurately recommend products meeting the user expectations from the products full of Linglan is a key for e-commerce operators to realize personalized and customized services, improve the service quality and enhance the user viscosity.
The determination method of the current recommended product comprises the following steps: and obtaining the preference score of the target user on the product, determining a neighbor user with higher similarity with the target user according to the preference score, and determining the recommended product according to the preference score of the neighbor user on the product.
For example: the product is audio-visual product, book, household electrical appliance, dress and fruit, and the preference grade adopts five preference levels: "specially dislike""dislike", "general", "like", and "special like", the corresponding scores were scored as 1, 2, 3, 4, and 5, and no evaluation was scored as 0. Assume the user is u1,u2,u3,u4And scoring the preference of each user for each product, as shown in table 1:
TABLE 1
Audio-visual product Book with detachable cover Household electrical appliance Dress ornament Fruit
u1 5 5 5 0 0
u2 5 5 5 0 0
u3 0 5 5 4 5
u4 0 5 5 1 0
If the recommended user is u4It scores books, appliances and decorations, and does not score audio-visual products and fruits, but u4Apples are of interest in fruit.
Then according to u shown in Table 14Is determined with u4The neighbor user with higher similarity is u3According to u3Scoring the product preference determines the recommended product to be one or more of a book, appliance, clothing, and fruit. For direction u4Scenario recommending fruit, although u4Of the fruits, apples are of interest only, but belong to the fruit, and therefore, u is the direction4Recommended fruit meet u4The actual situation of (1).
With the gradual refinement of product classification, the method has the defect of inaccurate product recommendation.
For example: books are refined into textbooks, biographies and novels, clothes are refined into skirts, trousers and shirts, and fruit products are refined into bananas, apples and grapes.
{u1,u2,u3,u4The preference scores for each product, as shown in table 2:
table 2
Figure BDA0001074303940000021
If the target user is still u4It scores textbooks, biographies, novels, home appliances and grapes, does not score audio-visual products, skirts, trousers, shirts, bananas and apples, but u is4Apples are of interest.
The above method, according to u shown in Table 24Is determined with u4The neighbor user with higher similarity is u1And u2According to u1And u2Scoring the product preference determines that the recommended product is one or more of an audio-visual product, a textbook, a biographical, a novel, and an appliance, but not u4Apple is recommended. And actually u4Interested in apples, recommended products and u4Does not match the actual situation.
Disclosure of Invention
In order to improve the accuracy of the determined recommended product, the embodiment of the application provides a method and a device for determining the recommended product.
In one aspect, an embodiment of the present application provides a method for determining a recommended product, where the method includes:
determining product categories in which the target user is interested;
acquiring neighbor users interested in the product categories interested in the target user;
and determining the recommended products recommended to the target user according to the interest degree of the neighbor users to the products to be recommended.
Optionally, before the determining the product category in which the target user is interested, the method further includes:
determining the interest degree of each user in each product category according to the number of the products evaluated by each user in each product category, wherein the users comprise target users and other users;
determining product categories of interest to the target user, comprising:
determining product categories in which target users are interested according to the interest degrees of the target users in the product categories;
the obtaining neighbor users interested in the product categories in which the target user is interested comprises:
and determining other users interested in the product categories interested in the target user as neighbor users.
Optionally, the determining the interest level of each user in the product category according to the number of the products evaluated by each user in each product category includes:
and determining the interest degree of each user in each product category according to the number of the products evaluated by each user in each product category and the number of the products included in each product category.
Optionally, the determining, according to the number of products evaluated by each user in each product category and the number of products included in each product category, the interest level of each user in each product category includes:
according to
Figure BDA0001074303940000031
Determining the interest degree of each user in each product category;
wherein, aijFor the interest level of user i in product category j, MijFor the number of products in product category j that are evaluated by user i,
Figure BDA0001074303940000032
for the total number of products evaluated by user i, N, in each product categoryjFor the number of products included in the product category j,
Figure BDA0001074303940000033
the total number of products included for each product category.
Optionally, the product to be recommended belongs to a product category in which the target user is interested.
Optionally, the determining, according to the interest level of the neighbor user in the product to be recommended, a recommended product recommended to the target user includes:
determining the recommendation degree of the product to be recommended aiming at the target user according to the interest degree of the neighbor user in the product to be recommended;
and determining a recommended product recommended to the target user according to the recommendation degree.
Optionally, the determining, according to the interest level of the neighbor user in the product to be recommended, the recommendation level of the product to be recommended for the target user includes:
and determining the recommendation degree of the product to be recommended for the target user according to the interest degree of each neighbor user to the product to be recommended, the similarity between the target user and each neighbor user and the sum of the similarities between the target user and each neighbor user.
Optionally, the determining the recommendation degree of the product to be recommended for the target user according to the sum of the interest degree of each neighbor user to the product to be recommended, the similarity between the target user and each neighbor user, and the similarity between the target user and each neighbor user includes:
according to
Figure BDA0001074303940000041
Determining the recommendation degree of the product to be recommended for the target user;
wherein r iss,jFor the recommendation degree r of the product j to be recommended aiming at the target user skjInterest degree of the product j to be recommended for the neighbor user k, sim (s, k) is similarity between the first target user s and the neighbor user k,
Figure BDA0001074303940000042
is the sum of the similarities between the target user s and each neighbor user.
Optionally, the determining, according to the interest level of the neighbor user in the product to be recommended, the recommendation level of the product to be recommended for the target user includes:
and determining the recommendation degree of the product to be recommended for the target user according to the interest degree of the product to be recommended of each neighbor user, the similarity between the target user and each neighbor user, the sum of the similarity between the target user and each neighbor user, the interest degree of the product category to which the product to be recommended of the target user belongs and the sum of the interest degrees of the product categories of the target user.
Optionally, the determining, according to the interest degree of each neighbor user in a product to be recommended, the similarity between the target user and each neighbor user, the sum of the similarity between the target user and each neighbor user, the interest degree of the target user in a product category to which the product to be recommended belongs, and the sum of the interest degrees of the target user in each product category, the recommendation degree of the product to be recommended for the target user is determined, includes:
according to
Figure BDA0001074303940000051
Determining the recommendation degree of the product to be recommended for the target user;
wherein r iss,jFor the recommendation degree r of the product j to be recommended aiming at the target user skjInterest degree of the product j to be recommended for the neighbor user k, sim (s, k) is similarity between the target user s and the neighbor user k,
Figure BDA0001074303940000052
is the sum of the similarities between the target user s and the neighbor users, as,jqThe interest degree of the target user s in the product category q to which the to-be-recommended product j belongs,
Figure BDA0001074303940000053
and the sum of the interest degrees of the target user s in each product category is shown, wherein w is the product category.
In another aspect, an embodiment of the present application provides an apparatus for determining a recommended product, where the apparatus includes:
the first determination module is used for determining the product category in which the target user is interested;
the acquisition module is used for acquiring neighbor users interested in the product categories which are determined by the first determination module and are interested in the target user;
and the second determination module is used for determining the recommended products recommended to the target user according to the interest degrees of the neighbor users to the products to be recommended, which are acquired by the acquisition module.
Optionally, the apparatus further comprises:
the third determining module is used for determining the interest degree of each user in each product category according to the number of the products evaluated by each user in each product category, wherein the users comprise target users and other users;
the first determining module is used for determining the product categories in which the target user is interested according to the interest degree of the target user in each product category determined by the third determining module;
and the acquisition module is used for determining other users interested in the product categories which are determined by the third determination module and are interested in the target user as neighbor users.
Optionally, the third determining module is configured to determine the interest level of each user in each product category according to the number of products evaluated by each user in each product category and the number of products included in each product category.
Optionally, the third determining module is configured to determine according to
Figure BDA0001074303940000061
Determining the interest degree of each user in each product category;
wherein, aijFor the interest level of user i in product category j, MijFor the number of products in product category j that are evaluated by user i,
Figure BDA0001074303940000062
for the total number of products evaluated by user i, N, in each product categoryjFor the number of products included in the product category j,
Figure BDA0001074303940000063
the total number of products included for each product category.
Optionally, the product to be recommended belongs to a product category in which the target user is interested.
Optionally, the second determining module includes:
the first determining unit is used for determining the recommendation degree of the product to be recommended aiming at the target user according to the interest degree of the neighbor user in the product to be recommended;
and the second determining unit is used for determining the recommended products recommended to the target user according to the recommendation degree determined by the first determining unit.
Optionally, the first determining unit is configured to determine the recommendation degree of the product to be recommended for the target user according to a sum of the interest degree of each neighbor user for the product to be recommended, the similarity between the target user and each neighbor user, and the similarity between the target user and each neighbor user.
Optionally, the first determining unit is configured to determine according to
Figure BDA0001074303940000064
Determining the recommendation degree of the vertical product to be recommended aiming at the target user;
wherein r iss,jFor the recommendation degree r of the product j to be recommended aiming at the target user skjInterest degree of the product j to be recommended for the neighbor user k, sim (s, k) is similarity between the first target user s and the neighbor user k,
Figure BDA0001074303940000065
is the sum of the similarities between the target user s and each neighbor user.
Optionally, the first determining unit is configured to determine the recommendation degree of the product to be recommended for the target user according to the interest degree of each neighbor user in the product to be recommended, the similarity between the target user and each neighbor user, the sum of the similarities between the target user and each neighbor user, the interest degree of the product category to which the product to be recommended by the target user belongs, and the sum of the interest degrees of the product categories by the target user.
Optionally, the first determining unit is configured to determine according to
Figure BDA0001074303940000071
Determining the recommendation degree of the product to be recommended for the target user;
wherein r iss,jFor the recommendation degree r of the product j to be recommended aiming at the target user skjInterest degree of the product j to be recommended for the neighbor user k, sim (s, k) is similarity between the target user s and the neighbor user k,
Figure BDA0001074303940000072
is the sum of the similarities between the target user s and the neighbor users, as,jqThe interest degree of the target user s in the product category q to which the to-be-recommended product j belongs,
Figure BDA0001074303940000073
and the sum of the interest degrees of the target user s in each product category is shown, wherein w is the product category.
The beneficial effects are as follows:
determining product categories in which the target user is interested; acquiring neighbor users interested in the product categories interested in the target user; and determining the recommended products recommended to the target user according to the interest degrees of the neighbor users to the products to be recommended. In the application, the neighbor users and the target users are interested in the same product category, and the recommended products determined according to the preferences of the neighbor users are products in the product category interested by the target users. Therefore, compared with the prior art, the finally determined recommended product is more in line with the preference of the target user, and the recommendation accuracy can be improved.
Drawings
Specific embodiments of the present application will be described below with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart illustrating another method for determining recommended products according to another embodiment of the present application;
fig. 2 is a schematic structural diagram of a recommended product determining apparatus according to another embodiment of the present application;
fig. 3 is a schematic structural diagram of another recommended product determining apparatus according to another embodiment of the present application;
fig. 4 shows a schematic structural diagram of a second determining module according to another embodiment of the present application.
Detailed Description
In order to make the technical solutions and advantages of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and not an exhaustive list of all embodiments. And the embodiments and features of the embodiments in the present description may be combined with each other without conflict.
When making a product recommendation, it is first necessary to determine the recommended product. In the prior art, preference scores of a target user for all products are obtained, neighbor users with higher similarity to the target user are determined according to the preference scores, and recommended products are determined according to the preference scores of the neighbor users for the products. The determination of the neighbor users is related to the preference scores of the target user for all products, along with the gradual refinement of product classification, the preference of the neighbor users determined according to the preference scores of the target user for all products is not enough to represent the preference of the target user, and the recommended products determined according to the preference of the neighbor users do not belong to the product categories in which the target user is interested, so that the recommendation accuracy of the method is reduced.
In order to improve recommendation accuracy, the method for determining the recommended products is provided, and the method for determining the product categories which are interested by the target user is used; acquiring neighbor users interested in the product categories interested in the target user; and determining the recommended products recommended to the target user according to the interest degrees of the neighbor users to the products to be recommended. According to the method, the neighbor users and the target user are interested in the same product category, the recommended products determined according to the preference of the neighbor users in the method belong to the product category interested by the target user, and the recommendation accuracy is improved.
In combination with the above implementation environment, the present application provides a method for determining a recommended product. The method provided by the application can be applied to various scenes to recommend the recommended product to the recommended user after the recommended product is determined, so that the personalized and customized requirements of the user are met, the viscosity of the user is improved, and the competitiveness is enhanced.
In order to more clearly and clearly illustrate the determination method of the recommended product provided by the embodiment, the following scenario is taken as an example in the embodiment, and for other scenarios, the implementation manner of the following scenario can be referred to.
Specific scenes are as follows: the product categories are 5 categories, which are respectively: audio-visual goods, books, household electrical appliances, dress and fruit, wherein, audio-visual goods category and household electrical appliances category do not refine concrete product, and the books category is refined into 3 kinds of products, is respectively: textbooks, biographies, and novels. The clothing category is refined into 3 products, which are respectively: skirt, pants, and shirt. The fruit category is refined into 3 products, which are respectively: banana, apple and grape. Preference scoring takes five preference levels: "particularly dislike", "general", "like", "particularly like", the corresponding scores were scored as 1, 2, 3, 4, 5, and no evaluation was scored as 0. Historical users are { u }1,u2,u3,u4And the preference scores of the historical users for the products are shown in table 3:
TABLE 3
Figure BDA0001074303940000091
The relationship between the determination device for determining the recommended product for executing the method provided by the embodiment and the storage device for storing the related data in the scene may be one of the following relationships:
the first relationship: the determination device for recommending the product and the storage device are the same device;
namely, the determination method of the recommended product provided by the embodiment is run on the storage device for storing the related data in the scene.
In this connection, the determination device for determining the recommended product, which operates the method provided by the present embodiment, directly reads the stored scene-related data. For example: each historical user, each product category, each product, the inclusion relationship between the product categories and the products, and preference scoring data of each product.
The second relationship: the determination device and the storage device of the recommended product are different devices;
the storage device for storing the related data in the scene and the device for determining the recommended product for running the method provided by the embodiment are two devices.
In this connection, the determination device for the recommended product, which operates the method provided in this embodiment, obtains the scene-related data from the storage device that stores the related data in the scene. For example, historical users, product categories, products, inclusion relationships between product categories and products, preference scoring data for products.
In addition, for the above scenario, no matter what kind of relationship is between the determination device of the recommended product running the method provided by the embodiment and the storage device storing the related data in the above scenario, when determining the recommended product, the following 2 cases may exist in the relationship between the target user and the historical user:
case 1: historical users do not include target users, e.g., target user is u5
For this case, u is not shown in Table 35Scoring the preference of each product, u cannot be determined5Therefore, the determination method of recommended products provided by the present application is not performed.
In specific implementation, a default product may be determined as a recommended product, and u is to be recorded in table 35After the preference of one or some products is scored, the method provided by the application is executed to determine recommended products.
Case 2: historical users include target users, e.g., target user is u4
For such a case, the method provided by the present application may be performed to determine the recommended product.
The target user is u below4And u is4For an example of apple interest, referring to fig. 1, a method for determining a recommended product provided in this embodiment will be described.
101: determining the interest degree of each historical user in each product category according to the number of the products evaluated by each historical user in each product category;
wherein the historical users comprise target users u4And other users u1,u2,u3
When the step is specifically implemented, the interest degree of each historical user in each product category can be determined according to the number of the products evaluated by each historical user in each product category and the number of the products included in each product category.
E.g. ui(any historical user) pair LjInterestingness of (any product category)
Figure BDA0001074303940000111
Wherein M isijFor the number of products in product category j that are rated by the historical user i,
Figure BDA0001074303940000112
for the total number of products in each product category, N, evaluated by the historical user ijFor the number of products included in the product category j,
Figure BDA0001074303940000113
the total number of products included for each product category.
Referring to Table 3, now for u1,u2,u3,u4The method of calculating the interestingness of each product category will be described in detail.
1) For u1:u1The evaluation of each product is shown in table 4.
TABLE 4
Figure BDA0001074303940000114
As can be seen from Table 4, u is assigned to the class L11The number of products evaluated was 1, and was u in the category L21The number of products evaluated was 3, and was rated by u in the category L31The number of products evaluated was 1, and was u in the category L41Number of products evaluatedAmount of 0, by u in class L51The number of products evaluated was 0. Thus, M is determined11=1,M12=3,M13=1,M14=0,M150. Also, as can be seen from Table 4, L1Including the number of products N1=1,L2Including the number of products N2=3,L3Including the number of products N3=1,L4Including the number of products N4=3,L5Including the number of products N5=3。
Therefore, the temperature of the molten metal is controlled,
Figure BDA0001074303940000115
Figure BDA0001074303940000116
Figure BDA0001074303940000117
Figure BDA0001074303940000118
Figure BDA0001074303940000121
2) for u2:u2The evaluation of each product is shown in table 5.
TABLE 5
Figure BDA0001074303940000122
As can be seen from Table 5, u is assigned to the class L12The number of products evaluated was 1, and was u in the category L22The number of products evaluated was 3, and was rated by u in the category L32The number of products evaluated was 1, and was u in the category L42The number of products evaluated was 0, and was u in the category L52The number of products evaluated was 0. Thus, M is determined21=1,M22=3,M23=1,M24=0,M250. Also, as can be seen from Table 5, L1Including the number of products N1=1,L2Including the number of products N2=3,L3Including the number of products N3=1,L4Including the number of products N4=3,L5Including the number of products N5=3。
Therefore, the temperature of the molten metal is controlled,
Figure BDA0001074303940000123
Figure BDA0001074303940000124
Figure BDA0001074303940000125
Figure BDA0001074303940000126
Figure BDA0001074303940000127
3) for u3:u3The evaluation of each product is shown in table 6.
TABLE 6
Figure BDA0001074303940000128
As can be seen from Table 6, u is assigned to the class L13The number of products evaluated was 0, and was u in the category L23The number of products evaluated was 1, and was u in the category L33The number of products evaluated was 1, and was u in the category L43The number of products evaluated was 3, and was rated by u in the category L53The number of products evaluated was 3. Thus, M is determined31=0,M32=1,M33=1,M34=3,M353. Also, as can be seen from Table 6, L1Including the number of products N1=1,L2Including the number of products N2=3,L3Including the number of products N3=1,L4Including the number of products N4=3,L5Including the number of products N5=3。
Therefore, the temperature of the molten metal is controlled,
Figure BDA0001074303940000131
Figure BDA0001074303940000132
Figure BDA0001074303940000133
Figure BDA0001074303940000134
Figure BDA0001074303940000135
4) for u4:u4The evaluation of each product is shown in table 7.
TABLE 7
Figure BDA0001074303940000136
As can be seen from Table 7, u is assigned to the class L14The number of products evaluated was 0, and was u in the category L24The number of products evaluated was 3, and was rated by u in the category L34The number of products evaluated was 1, and was u in the category L44The number of products evaluated was 0, and was u in the category L54The number of products evaluated was 1. Thus, M is determined41=0,M42=3,M43=1,M44=0,M451. Also, as can be seen from Table 7, L1Including the number of products N1=1,L2Including the number of products N2=3,L3Including the number of products N3=1,L4Including the number of products N4=3,L5Including the number of products N5=3。
Therefore, the temperature of the molten metal is controlled,
Figure BDA0001074303940000137
Figure BDA0001074303940000141
Figure BDA0001074303940000142
Figure BDA0001074303940000143
Figure BDA0001074303940000144
in conclusion, the interest degree matrix of each historical user to each product category is obtained
Figure BDA0001074303940000145
Obviously, when aij0 denotes user uiProduct class L has not yet been evaluatedj. In the case of product classification and product quantity determination, uiTo LjThe greater the number of products evaluated, the greater aijThe larger u is considered to beiTo LjThe greater the interest.
In addition, after the interestingness matrix of each historical user for each product category is obtained, interesting users interested in any product category can be determined according to the interestingness of each historical user for each product category.
Specifically, an interest preference scale parameter alpha is determined, wherein alpha is greater than 0, and when 0 is greater than aijWhen < alpha, u is considered to beiTo LjInterest is general, even small and negligible; when a isijWhen u is not less than alpha, u is considered to beiTo LjThere is an interest preference, and the greater the value the greater the interest preference.
For a predetermined α, will be for Lj(j ═ 1, 2, …, m) users with interest preferences are put into a category, i.e. users who are interested in each product category:
Uj={ui|ai,jand the value is more than or equal to alpha, i is 1, 2, …, N, wherein N is the number of users.
Using the interest degree of each historical user in each product category as an interest degree matrix
Figure BDA0001074303940000151
α ═ 1 is an example: l is1User of interest U1Is { u1,u2},L2User of interest U2Is { u1,u2,u3,u4},L3User of interest U3Is { u1,u2,u3,u4},L4User of interest U4Is { u3},L5User of interest U5Is { u3,u4}。
Of course, in obtaining LjUser of interest UjThen, the sum of the obtained sum and U can be obtainedjCorresponding scoring matrices, i.e. UjIn each historical user pair LjThe scoring matrix of (a) is calculated,
Figure BDA0001074303940000152
e.g. U1In each historical user pair L1Scoring matrix of
Figure BDA0001074303940000153
U2In each historical user pair L2Scoring matrix of
Figure BDA0001074303940000154
U3In each historical user pair L3Scoring matrix of
Figure BDA0001074303940000155
U4In each historical user pair L4Scoring matrix of
Figure BDA0001074303940000156
U5In each historical user pair L5Scoring matrix of
Figure BDA0001074303940000157
Step 101 is not a step that needs to be executed each time the determination method of the recommended product provided by this embodiment is executed, and only when the determination method of the recommended product provided by this embodiment is executed for the first time, or the historical user changes, or the product category changes, or the corresponding relationship between the product and the product category changes, or the preference score of any product of the historical user changes, step 101 is executed to determine the latest interest level of the historical user in the product category, and this embodiment does not limit the trigger condition for executing step 101.
102: determining product categories in which the target user is interested;
the target user and the historical user have 2 relations, and the historical user does not include the target user, or the historical user includes the target user. The present embodiment is described only for the case where the history user includes the target user.
Specifically, the historical users include target users and other users, the interest-degree of the historical users in the product categories obtained in step 101 also includes the interest-degree of the target users in each product category and the interest-degree of the other users in each product category, and the product categories in which the target users are interested are determined according to the interest-degree of the target users in each product category obtained in step 101.
Taking the example in step 101 as an example, the target user u4The interest degrees for each product category are respectively: a is41=0、
Figure BDA0001074303940000161
a44=0、
Figure BDA0001074303940000162
If the interest preference scale parameter alpha is larger than 0, when aijWhen u is not less than alpha, u is considered to beiTo LjPreference of interest, then u is determined4The product category of interest is L2、L3And L5
103: determining whether the product to be recommended belongs to the product category which is interested by the target user, if the product to be recommended does not belong to the product category which is interested by the target user, executing step 104, and if the product to be recommended belongs to the product category which is interested by the target user, executing step 105 and the subsequent steps;
the product to be recommended in this embodiment may be a product that is not scored by the target user, or may be a product that is scored by the target user.
For example, the product to be recommended may be u4Unscored audiovisual product, skirt, pants, shirt, banana or apple by orienting u4Recommending unscored products, can be4Find other products of interest, raise the user's viscosity. The product to be recommended may also be u4Scored textbook, biography, novel, household appliance, grape, by u4Recommending the scored product, and making u4The latest dynamic of the interested product is found, and the viscosity of the user is improved.
Take shopping platform as an example, u4Purchasing textbooks, biographies, novels, household appliances and grapes, and grading, wherein if the products to be recommended are determined to be audio-visual products, skirts, trousers, shirts, bananas or apples, u can be added4The possibility of purchasing audio-video products, skirts, trousers, shirts, bananas or apples is improved4Viscosity to shopping platform. In addition, u can be used for sales promotion of scientific education books, biographies, novels, household appliances or grapes4The purchased and scored textbook, biographical, novel, home appliance or grape is determined again as the product to be recommended so that u is4Obtaining the sales promotion information of textbooks, biographies, novels, household appliances or grapes and increasing u4Repurchase of textbook, biography, novel, and familyElectricity or grape potential, increasing user viscosity.
For the specific scenario in this embodiment, if the product to be recommended is an audio-visual product, the corresponding product category is L1Do not belong to u4The product category of interest, step 104 is executed. If the product to be recommended is a skirt, the corresponding product category is L3Do not belong to u4The product category of interest, step 104 is executed. If the product to be recommended is trousers, the corresponding product type is L3Do not belong to u4The product category of interest, step 104 is executed. If the product to be recommended is a shirt, the corresponding product category is L3Do not belong to u4The product category of interest, step 104 is executed.
If the product to be recommended is a textbook, the corresponding product category is L2Is u of4The product category of interest, step 105 is performed. If the product to be recommended is a biography, the corresponding product category is L2Is u of4The product category of interest, step 105 is performed. If the product to be recommended is a novel, the corresponding product category is L2Is u of4The product category of interest, step 105 is performed. If the product to be recommended is a household appliance, the corresponding product category is L3Is u of4The product category of interest, step 105 is performed. If the product to be recommended is banana, the corresponding product category is L5Is u of4The product category of interest, step 105 is performed. If the product to be recommended is apple, the corresponding product category is L5Is u of4The product category of interest, step 105 is performed. If the product to be recommended is grape, the corresponding product category is L5Is u of4The product category of interest, step 105 is performed.
104: determining that the interest degree of the target user to the recommended product is 0, and ending the process;
taking the product to be recommended as an audio-video product as an example, u is determined4The interest degree of the audio-video product is 0, i.e. the audio-video product is not recommended to u4And (4) recommending the product, and ending the process.
105: acquiring neighbor users interested in the product categories interested in the target user;
wherein, the historical users include target users and other users, the interest-degree of the historical users on the product categories obtained in step 101 also includes the interest-degree of the target users on each product category and the interest-degree of the other users on each product category, and in this step, according to the interest-degree of the other users on each product category obtained in step 101, the target users (u) determined in step 102 will be selecteds) Product category of interest (L)w) Other users of interest (US)sw) Determined to be a neighbor user.
Taking the example in step 102 as an example, in step 102, u is determined4The product category of interest is L2、L3And L5Obtained in step 101
Figure BDA0001074303940000181
a14=0、a15=0;
Figure BDA0001074303940000182
Figure BDA0001074303940000183
a24=0、a25=0;a31=0、
Figure BDA0001074303940000184
If the interest preference scale parameter alpha is larger than 0, when aijWhen u is not less than alpha, u is considered to beiTo LjPreference of interest, then u is determined1The product category of interest is L1、L2And L3Determining u2The product category of interest is L1、L2And L3Determining u3The product category of interest is L2、L3And L5. In step 102, a target user u is determined4The product category of interest is L2、L3And L5Then, to u4Product category of interest L2Interested neighbor user US42Is { u1,u2,u3Is to u4Product category of interest L3Interested neighbor user US43Is { u1,u2,u3Is to u4Product category of interest L5Interested neighbor user US45Is { u3}。
106: and determining the recommended products recommended to the target user according to the interest degrees of the neighbor users to the products to be recommended.
Specific embodiments of this step include, but are not limited to: step 1: determining the recommendation degree of a product to be recommended aiming at a target user according to the interest degree of a neighbor user in the product to be recommended; step 2: and determining a recommended product recommended to the target user according to the recommendation degree.
The following describes the implementation of each step.
Step 1: determining the recommendation degree of a product to be recommended aiming at a target user according to the interest degree of a neighbor user in the product to be recommended;
for step 1, implementations include, but are not limited to, the following two:
implementation mode 1: and determining the recommendation degree of the product to be recommended for the target user according to the interest degree of the product to be recommended by each neighbor user, the sum of the similarity between the target user and each neighbor user and the similarity between the target user and each neighbor user.
Implementation manner 2: and determining the recommendation degree of the product to be recommended for the target user according to the interest degree of the product to be recommended by each neighbor user, the similarity between the target user and each neighbor user, the sum of the similarity between the target user and each neighbor user, the interest degree of the product category to which the product to be recommended by the target user belongs and the sum of the interest degrees of the product categories by the target user.
For the implementation mode 1, the recommendation degree of the product to be recommended for the target user is determined according to the sum of the interest degree of each neighbor user to the product to be recommended, the similarity between the target user and each neighbor user and the similarity between the target user and each neighbor user.
Detailed description of the inventionCan be according to
Figure BDA0001074303940000191
And determining the recommendation degree of the product to be recommended aiming at the target user.
Wherein r iss,jFor the recommendation degree r of the product j to be recommended aiming at the target user skjInterest degree of the product j to be recommended for the neighbor user k, sim (s, k) is similarity between the first target user s and the neighbor user k,
Figure BDA0001074303940000192
is the sum of the similarities between the target user s and each neighbor user.
sim (s, k) may be determined by any conventional similarity calculation method, and is not limited herein.
For example: for usOther users USswAny other user u inkSim (s, k) is obtained from the cloud vector V ═ Er (Er, En, He).
In particular, the method comprises the following steps of,
1) calculating a sample mean value:
Figure BDA0001074303940000193
wherein M is the number of products;
2) calculate first order sample absolute center:
Figure BDA0001074303940000194
3) calculating the sample variance:
Figure BDA0001074303940000195
4)Ersestimated value of (a):
Figure BDA0001074303940000196
5) super entropy HesEstimated value of (a):
Figure BDA0001074303940000197
6) entropy EnsEstimated value of (a):
Figure BDA0001074303940000198
7) to obtain usCloud vector V ofs=(Ers,Ens,Hes);
8) Repetition of 1) to 7) gave a cloud vector V of ukk=(Erk,Enk,Hek);
9) According to VsAnd VkObtaining sim (s, k) ═ cos (V)s,Vk)。
Referring to table 3, taking the products to be recommended as textbooks, biographies, novels, household appliances, bananas, apples and grapes as examples, the interestingness of the products to be recommended according to the neighbor users is determined for the products to be recommended, and the products to be recommended are determined according to the interestingness of the products to be recommended4The 1 st implementation of the recommendation degree of (1) will be described.
In step 102 u is determined4The product category of interest is L2、L3And L5In step 105, pairs u are determined4Product category of interest L2Interested neighbor user US42Is { u1,u2,u3Is to u4Product category of interest L3Interested neighbor user US43Is { u1,u2,u3Is to u4Product category of interest L5Interested neighbor user US45Is { u3}。
1) The product 1 to be recommended is a science and education book
The product category corresponding to the science book is L2,US42Is { u1,u2,u3If u4And u1The similarity between the two is sim (4, 1), u4And u2The similarity between the two is sim (4, 2), u4And u3The similarity between the two is sim (4, 3), then the textbook is determined to be directed to u4Degree of recommendation of
Figure BDA0001074303940000201
2) The product 2 to be recommended is a biographical
The product category corresponding to the biography is L2,US42Is { u1,u2,u3Pointing to u by biography4Degree of recommendation of
Figure BDA0001074303940000202
3) The product to be recommended 3 is a novel
The product category corresponding to the novel is L2,U542Is { u1,u2,u3Let us say u4Degree of recommendation of
Figure BDA0001074303940000203
4) The product 4 to be recommended is a household appliance
The product category corresponding to the household appliance is L3,US43Is { u1,u2,u3The household appliance aims at u4Degree of recommendation of
Figure BDA0001074303940000204
5) The product to be recommended 5 is banana
The corresponding product category of the bananas is L5,US45Is { u3F. banana for u4Degree of recommendation of
Figure BDA0001074303940000211
6) The product 6 to be recommended is apple
The corresponding product category of the apple is L5,US45Is { u3Apple for u4Degree of recommendation of
Figure BDA0001074303940000212
7) The product 7 to be recommended is grape
The product category corresponding to the grapes is L5,US45Is { u3Grape for u4Degree of recommendation of
Figure BDA0001074303940000213
From this, it can be seen that novels, home appliances, bananas, apples and grapes are directed to u4The recommendation degree of (1) is 5, and textbooks and biographies are directed at u4The recommendation degrees are all less than 5.
Implementation manner 2: and determining the recommendation degree of the product to be recommended for the target user according to the interest degree of the product to be recommended by each neighbor user, the similarity between the target user and each neighbor user, the sum of the similarity between the target user and each neighbor user, the interest degree of the product category to which the product to be recommended by the target user belongs and the sum of the interest degrees of the product categories by the target user.
For example: according to
Figure BDA0001074303940000214
Determining the recommendation degree of a product to be recommended aiming at a target user;
wherein r iss,jFor the recommendation degree r of the product j to be recommended aiming at the target user skjInterest degree of the product j to be recommended for the neighbor user k, sim (s, k) is similarity between the target user s and the neighbor user k,
Figure BDA0001074303940000215
is the sum of the similarities between the target user s and the neighbor users, as,jqThe interest degree of the target user s in the product category q to which the to-be-recommended product j belongs,
Figure BDA0001074303940000216
and the sum of the interest degrees of the target user s in each product category is shown, wherein w is the product category.
Still referring to table 3, taking the products to be recommended as textbooks, biographies, novels, household appliances, bananas, apples and grapes as examples, the interestingness of the products to be recommended according to the neighbor users is determined for u4The 2 nd implementation of the recommendation degree of (1) is exemplified.
In step 102, it is determinedu4The product category of interest is L2、L3And L5In step 105, the product category L of interest to u4 is determined2Interested neighbor user US42Is { u1,u2,u3Is to u4Product category of interest L3Interested neighbor user US43Is { u1,u2,u3Is to u4Product category of interest L5Interested neighbor user US45Is { u3}。
1) The product 1 to be recommended is a science and education book
The product category corresponding to the science book is L2,US42Is { u1,u2,u3If u4And u1The similarity between the two is sim (4, 1), u4And u2The similarity between the two is sim (4, 2), u4And u3The similarity between the two is sim (4, 3), then the determination is made
Figure BDA0001074303940000221
A is obtained in step 10141=0、
Figure BDA0001074303940000222
a44=0、
Figure BDA0001074303940000223
Thus, the textbook is determined for u4Degree of recommendation of
Figure BDA0001074303940000224
2) The product 2 to be recommended is a biographical
The product category corresponding to the biography is L2,US42Is { u1,u2,u3Is determined
Figure BDA0001074303940000225
Determining a discipline for u4Degree of recommendation of
Figure BDA0001074303940000226
3) The product to be recommended 3 is a novel
The product category corresponding to the novel is L2,US42Is { u1,u2,u3Is determined
Figure BDA0001074303940000227
Determining novel for u4Degree of recommendation of
Figure BDA0001074303940000231
4) The product 4 to be recommended is a household appliance
The product category corresponding to the household appliance is L3,US43Is { u1,u2,u3Is determined
Figure BDA0001074303940000232
Determining household appliance target u4Degree of recommendation of
Figure BDA0001074303940000233
5) The product to be recommended 5 is banana
The corresponding product category of the bananas is L5,US45Is { u3Is determined
Figure BDA0001074303940000234
Determining banana target u4Degree of recommendation of
Figure BDA0001074303940000235
6) The product 6 to be recommended is apple
The corresponding product category of the apple is L5,US45Is { u3Is determined
Figure BDA0001074303940000236
Determining apple target u4Degree of recommendation of
Figure BDA0001074303940000237
7) The product 7 to be recommended is grape
The product category corresponding to the grapes is L5,US45Is { u3Is determined
Figure BDA0001074303940000238
Determination of grape aim u4Degree of recommendation of
Figure BDA0001074303940000241
From this, it can be seen that the novel and household appliances aim at u4Are all recommended
Figure BDA0001074303940000242
Banana, apple and grape seed for u4Are all recommended
Figure BDA0001074303940000243
Textbook and biography are directed at u4Degree of recommendation of
Figure BDA0001074303940000244
After step 1 is performed: after determining the recommendation degree of the product to be recommended for the target user according to the interest degree of the neighbor user in the product to be recommended, executing the step 2: and determining a recommended product recommended to the target user according to the recommendation degree.
One of the specific implementation manners of step 2 may be to sort the products to be recommended from high to low according to the recommendation degree, and determine a preset number of products ranked in the top as recommended products recommended to the target user.
Taking the preset number of 5 as an example, if the step 1 is realized by the implementation manner 1, the novels, the household appliances, the bananas, the apples and the grapes aim at u4The recommendation degree of (1) is 5, and textbooks and biographies are directed at u4The recommendation degrees are all less than 5. Products to be recommended are sorted from big to small according to recommendation degrees into (1) novel, household appliances, bananas, apples and grapes and (2) textbooks and biographies, so that the top 5 products are sorted: novel, household electrical appliance, banana, apple, grape, determined as recommended to u4The recommended product of (1), including apple, with u4Match the actual situation of.
If step 1 is implemented by the implementation manner 2, the novel household appliance aims at u4Are all recommended
Figure BDA0001074303940000245
Banana, apple and grape seed for u4Are all recommended
Figure BDA0001074303940000246
Textbook and biography are directed at u4Degree of recommendation of
Figure BDA0001074303940000247
Products to be recommended are sorted from big to small according to recommendation degrees into (1) novel and household appliances, (2) bananas, apples and grapes and (3) textbooks and biographies, so that the top 5 products are sorted: novel, household electrical appliance, banana, apple, grape, determined as recommended to u4The recommended product of (1), including apple, with u4Match the actual situation of.
Therefore, no matter step 1 is realized by the first implementation mode, the recommendation to u is finally determined4The recommended products are all novels, household appliances, bananas, apples and grapes.
For the specific scenario in this embodiment, the prior art is based on u as shown in FIG. 34Scoring preference of all products, determining4The neighbor user with higher similarity is uAnd u2According to u1And u2Scoring the product preference determines that the recommended product is one or more of an audio-visual product, a textbook, a biographical, a novel, and an appliance, but not u4Apple is recommended. And actually u4Interest in apple, leading to the products recommended by the prior art and u4Does not match the actual situation.
The method provided by the embodiment finally determines the recommendation u4The recommended products are all novel, household appliances, bananas, apples and grapes which are oriented to u4Recommending apples so that the products and u recommended by the method provided by the embodiment4Match the actual situation of.
The recommended products obtained by the method provided by the embodiment are not determined as neighbor users according to all scores of the target user in the prior art, but are determined according to the product categories in which the target user is interested, so that the neighbor users and the target user are interested in the same product categories, and at least one recommended product exists in the recommended products determined according to the preference of the neighbor users and is a product in the product category in which the target user is interested, so that the accuracy of the determined recommended products is improved.
In practical application, a movie-related website, for which the number of users exceeds 70000 and the number of movies scored by the users exceeds 5000, performs product recommendation by using the method for determining recommended products provided in this embodiment. 21078 pieces of score data of 1489 movies of 274 users are extracted from the database, and the sparse level of the data set is as follows: 1-21078/(274 × 1489) ═ 0.9483.
And adopting a metric MAE (Mean Absolute Error) of the recommended result, wherein the smaller the MAE value, the higher the recommended quality.
Figure BDA0001074303940000251
Wherein p isiFor a predicted set of user scores, qiIs the actual set of scores. The MAE obtained by the method provided in this example was 0.77, and the MAE obtained by the prior art was 0.81.
Has the advantages that:
determining product categories in which the target user is interested; acquiring neighbor users interested in the product categories interested in the target user; and determining the recommended products recommended to the target user according to the interest degrees of the neighbor users to the products to be recommended. In the application, the neighbor users and the target users are interested in the same product category, and the recommended products determined according to the preferences of the neighbor users are products in the product category interested by the target users. Therefore, compared with the prior art, the finally determined recommended product is more in line with the preference of the target user, and the recommendation accuracy can be improved.
Based on the same inventive concept, the embodiment provides a device for determining a recommended product, and since the principle of solving the problem of the device is similar to the method for determining a recommended product shown in fig. 1, the implementation of the device can refer to the embodiment of the method shown in fig. 1, and repeated parts are not described again.
Referring to fig. 2, the apparatus includes:
a first determination module 201 for determining a product category in which a target user is interested;
an obtaining module 202, configured to obtain neighbor users interested in the product category that is determined by the first determining module 201 to be interested in by the target user;
the second determining module 203 is configured to determine a recommended product recommended to the target user according to the interest level of the neighbor user to the product to be recommended, which is obtained by the obtaining module 202.
Referring to fig. 3, the apparatus further comprises:
a third determining module 204, configured to determine, according to the number of products evaluated by each user in each product category, the interest level of each user in each product category, where the users include a target user and other users;
the first determining module 201 is configured to determine a product category in which the target user is interested according to the interest level of the target user in each product category determined by the third determining module 204;
an obtaining module 202, configured to determine other users interested in the product category determined by the third determining module 204 as interested in the target user as neighbor users.
Optionally, the third determining module 204 is configured to determine the interest level of each user in each product category according to the number of products evaluated by each user in each product category and the number of products included in each product category.
Optionally, a third determining module 204 for determining
Figure BDA0001074303940000261
Determining the interest degree of each user in each product category;
wherein, aijFor the interest level of user i in product category j, MijFor the number of products in product category j that are evaluated by user i,
Figure BDA0001074303940000262
for the total number of products evaluated by user i, N, in each product categoryjFor the number of products included in the product category j,
Figure BDA0001074303940000271
the total number of products included for each product category.
Wherein, the product to be recommended belongs to the product category which is interested by the target user.
Referring to fig. 4, the second determining module 203 includes:
the first determining unit 2031 is configured to determine, according to the interest degree of the neighbor user in the product to be recommended, a recommendation degree of the product to be recommended for the target user;
a second determining unit 2032, configured to determine a recommended product recommended to the target user according to the recommendation degree determined by the first determining unit 2031.
Optionally, the first determining unit 2031 is configured to determine, according to a sum of the interest degree of each neighboring user for the product to be recommended, the similarity between the target user and each neighboring user, and the similarity between the target user and each neighboring user, a recommendation degree of the product to be recommended for the target user.
Optionally, a first determining unit 2031 for determining according to
Figure BDA0001074303940000272
Determining the recommendation degree of a product to be recommended aiming at a target user;
wherein r iss,jFor the recommendation degree r of the product j to be recommended aiming at the target user skjInterest degree of the product j to be recommended for the neighbor user k, sim (s, k) is similarity between the first target user s and the neighbor user k,
Figure BDA0001074303940000273
is the sum of the similarities between the target user s and each neighbor user.
Optionally, the first determining unit 2031 is configured to determine the recommendation degree of the product to be recommended for the target user according to the interest degree of each neighbor user in the product to be recommended, the similarity between the target user and each neighbor user, the sum of the similarities between the target user and each neighbor user, the interest degree of the product category to which the product to be recommended by the target user belongs, and the sum of the interest degrees of the product categories by the target user.
Optionally, a first determining unit 2031 for determining according to
Figure BDA0001074303940000274
Determining the recommendation degree of a product to be recommended aiming at a target user;
wherein r iss,jFor the recommendation degree r of the product j to be recommended aiming at the target user skjInterest degree of the product j to be recommended for the neighbor user k, sim (s, k) is similarity between the target user s and the neighbor user k,
Figure BDA0001074303940000281
is the sum of the similarities between the target user s and the neighbor users, as,jqThe interest degree of the target user s in the product category q to which the to-be-recommended product j belongs,
Figure BDA0001074303940000282
and the sum of the interest degrees of the target user s in each product category is shown, wherein w is the product category.
The beneficial effects are as follows:
determining product categories in which the target user is interested; acquiring neighbor users interested in the product categories interested in the target user; and determining the recommended products recommended to the target user according to the interest degrees of the neighbor users to the products to be recommended. In the application, the neighbor users and the target users are interested in the same product category, and the recommended products determined according to the preferences of the neighbor users are products in the product category interested by the target users. Therefore, compared with the prior art, the finally determined recommended product is more in line with the preference of the target user, and the recommendation accuracy can be improved.
For convenience of description, each part of the above-described apparatus is separately described as being functionally divided into various modules or units. Of course, the functionality of the various modules or units may be implemented in the same one or more pieces of software or hardware in practicing the invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

Claims (14)

1. A method for determining a recommended product, the method comprising:
determining a product category in which a target user is interested, wherein the product category comprises a plurality of products;
acquiring neighbor users interested in the product categories interested in the target user;
determining a recommended product recommended to the target user according to the interest degree of the neighbor user to the product to be recommended, wherein the product to be recommended is a product in a product category in which the target user is interested;
the step of determining the recommended products recommended to the target user according to the interest degrees of the neighbor users in the products to be recommended comprises the following steps:
determining the recommendation degree of the product to be recommended aiming at the target user according to the interest degree of the neighbor user in the product to be recommended;
determining a recommended product recommended to the target user according to the recommendation degree;
the step of determining the recommendation degree of the product to be recommended according to the interest degree of the neighbor user in the product to be recommended comprises the following steps:
and determining the recommendation degree of the product to be recommended aiming at the target user according to the sum of the similarity degrees between the target user and each neighbor user, the interest degree of the product to be recommended of each neighbor user and the similarity degree between the target user and each neighbor user.
2. The method of claim 1, wherein prior to determining the product category of interest to the target user, further comprising:
determining the interest degree of each user in each product category according to the number of the products evaluated by each user in each product category, wherein the users comprise target users and other users;
determining product categories of interest to the target user, comprising:
determining product categories in which target users are interested according to the interest degrees of the target users in the product categories;
the obtaining neighbor users interested in the product categories in which the target user is interested comprises:
and determining other users interested in the product categories interested in the target user as neighbor users.
3. The method of claim 2, wherein determining the interest level of each user in the product category according to the number of products in each product category that are evaluated by each user comprises:
and determining the interest degree of each user in each product category according to the number of the products evaluated by each user in each product category and the number of the products included in each product category.
4. The method according to claim 3, wherein determining the interest level of each user in each product category according to the number of products evaluated by each user in each product category and the number of products included in each product category comprises:
according to
Figure FDA0002629574770000021
Determining the interest degree of each user in each product category;
wherein, aijFor the interest level of user i in product category j, MijFor the number of products in product category j that are evaluated by user i,
Figure FDA0002629574770000022
for the total number of products evaluated by user i, N, in each product categoryjFor the number of products included in the product category j,
Figure FDA0002629574770000023
the total number of products included for each product category.
5. The method according to claim 1, wherein the determining the recommendation degree of the product to be recommended for the target user according to the sum of the similarity degrees between the target user and each neighbor user, the interest degree of each neighbor user for the product to be recommended, and the similarity degrees between the target user and each neighbor user comprises:
according to
Figure FDA0002629574770000024
Determining the recommendation degree of the product to be recommended for the target user;
wherein r iss,jFor a product to be recommended j aim atRecommendation degree, r, of target user skjInterest degree of the product j to be recommended for the neighbor user k, sim (s, k) is similarity between the target user s and the neighbor user k,
Figure FDA0002629574770000025
is the sum of the similarities between the target user s and each neighbor user.
6. The method according to claim 1, wherein the determining the recommendation degree of the product to be recommended for the target user according to the interest degree of the neighbor user in the product to be recommended comprises:
and determining the recommendation degree of the product to be recommended aiming at the target user according to the similarity and the value between the target user and each neighbor user, the interest degree and the value of the target user to each product category, the interest degree of each neighbor user to the product to be recommended, the similarity between the target user and each neighbor user, and the interest degree of the target user to the product category to which the product to be recommended belongs.
7. The method according to claim 6, wherein the determining the recommendation degree of the product to be recommended for the target user according to the similarity and value between the target user and each neighbor user, the interest degree and value of the target user in each product category, the interest degree of each neighbor user in the product to be recommended, the similarity between the target user and each neighbor user, and the interest degree of the target user in the product category to which the product to be recommended belongs comprises:
according to
Figure FDA0002629574770000031
Determining the recommendation degree of the product to be recommended for the target user;
wherein r iss,jFor the recommendation degree r of the product j to be recommended aiming at the target user skjInterest degree of a product j to be recommended for a neighbor user k, sim (s, k) is similarity between a target user s and the neighbor user kThe degree of the magnetic field is measured,
Figure FDA0002629574770000032
is the sum of the similarities between the target user s and the neighbor users, as,jqThe interest degree of the target user s in the product category q to which the to-be-recommended product j belongs,
Figure FDA0002629574770000033
and the sum of the interest degrees of the target user s in each product category is shown, wherein w is the product category.
8. An apparatus for determining a recommended product, the apparatus comprising:
the system comprises a first determination module, a second determination module and a third determination module, wherein the first determination module is used for determining a product category which is interested by a target user, and the product category comprises a plurality of products;
the acquisition module is used for acquiring neighbor users interested in the product categories which are determined by the first determination module and are interested in the target user;
the second determination module is used for determining a recommended product recommended to the target user according to the interest degree of the neighbor user to the recommended product, which is acquired by the acquisition module, wherein the recommended product is a product in a product category in which the target user is interested;
the second determining module includes:
the first determining unit is used for determining the recommendation degree of the product to be recommended aiming at the target user according to the interest degree of the neighbor user in the product to be recommended;
the second determining unit is used for determining recommended products recommended to the target user according to the recommendation degree determined by the first determining unit;
the first determining unit is used for determining the recommendation degree of the product to be recommended for the target user according to the sum of the similarity degrees between the target user and each neighbor user, the interest degree of the product to be recommended of each neighbor user and the similarity degrees between the target user and each neighbor user.
9. The apparatus of claim 8, further comprising:
the third determining module is used for determining the interest degree of each user in each product category according to the number of the products evaluated by each user in each product category, wherein the users comprise target users and other users;
the first determining module is used for determining the product categories in which the target user is interested according to the interest degree of the target user in each product category determined by the third determining module;
and the acquisition module is used for determining other users interested in the product categories which are determined by the third determination module and are interested in the target user as neighbor users.
10. The apparatus of claim 9, wherein the third determining module is configured to determine the interest level of each user in each product category according to the number of products evaluated by each user in each product category and the number of products included in each product category.
11. The apparatus of claim 10, wherein the third determining module is configured to determine the second determination based on
Figure FDA0002629574770000041
Determining the interest degree of each user in each product category;
wherein, aijFor the interest level of user i in product category j, MijFor the number of products in product category j that are evaluated by user i,
Figure FDA0002629574770000042
for the total number of products evaluated by user i, N, in each product categoryjFor the number of products included in the product category j,
Figure FDA0002629574770000043
the total number of products included for each product category.
12. The apparatus of claim 8, wherein the first determining unit is configured to determine the first threshold according to
Figure FDA0002629574770000051
Determining the recommendation degree of the product to be recommended for the target user;
wherein r iss,jFor the recommendation degree r of the product j to be recommended aiming at the target user skjInterest degree of the product j to be recommended for the neighbor user k, sim (s, k) is similarity between the target user s and the neighbor user k,
Figure FDA0002629574770000052
is the sum of the similarities between the target user s and each neighbor user.
13. The apparatus according to claim 8, wherein the first determining unit is configured to determine the recommendation degree of the product to be recommended for the target user according to the similarity and value between the target user and each neighboring user, the interest degree and value of the target user in each product category, the interest degree of each neighboring user in the product to be recommended, the similarity between the target user and each neighboring user, and the interest degree of the target user in the product category to which the product to be recommended belongs.
14. The apparatus of claim 13, wherein the first determining unit is configured to determine the first threshold according to
Figure FDA0002629574770000053
Determining the recommendation degree of the product to be recommended for the target user;
wherein r iss,jFor the recommendation degree r of the product j to be recommended aiming at the target user skjInterest degree of the product j to be recommended for the neighbor user k, sim (s, k) is similarity between the target user s and the neighbor user k,
Figure FDA0002629574770000054
is the sum of the similarities between the target user s and the neighbor users, as,jqThe interest degree of the target user s in the product category q to which the to-be-recommended product j belongs,
Figure FDA0002629574770000055
and the sum of the interest degrees of the target user s in each product category is shown, wherein w is the product category.
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