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Item-based transfer learning recommendation method and recommendation apparatus thereof

A technology of transfer learning and recommendation methods, which is applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as the inability to further tap learners' interests and potentials, and difficulty in meeting learners' learning requirements, so as to improve density and accuracy, save user time, and solve the effect of information overload

Inactive Publication Date: 2016-03-30
TIANJIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, only recommending the same type, the same level, knowledge and similar knowledge to learners, it is difficult to meet the learning requirements of learners, and at the same time, it is impossible to further tap the interests and potential of learners

Method used

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  • Item-based transfer learning recommendation method and recommendation apparatus thereof
  • Item-based transfer learning recommendation method and recommendation apparatus thereof
  • Item-based transfer learning recommendation method and recommendation apparatus thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041] An item-based transfer learning recommendation method, see figure 1 , the recommended method includes the following steps:

[0042] 101: Establish user-item scoring matrices for the auxiliary domain and the target domain respectively;

[0043] Carry out user modeling on the auxiliary domain and the target domain, and extract user ratings for items according to the data model of the auxiliary domain or the target domain (for example: star rating, language description, and number of views, etc.), and establish the auxiliary domain and the target domain respectively The user-item rating matrix of , where the user-item rating matrix of the auxiliary domain is denoted as R aux , and the user-item rating matrix of the target domain is denoted as R. Specifically, the number of users is denoted as n, the number of items is denoted as m, and the rating matrix R is established, then the rating value of user i on item j is denoted as Rij , where i is the user number and satisfie...

Embodiment 2

[0056] The scheme in embodiment 1 is introduced in detail below in conjunction with specific calculation formulas and examples, see the following description for details:

[0057] 201: In the process of personalized recommendation, first use the user to model the rating relationship and rating value of the item, and establish the user-item rating matrix R of the auxiliary domain through user modeling aux , the user-item rating matrix R of the target domain;

[0058] The number of users is denoted as n, the number of items is denoted as m, then the rating value of user i on item j is denoted as R ij or (R aux ) ij , where i is the user number and satisfies 1≤i≤n, j is the user number and satisfies 1≤j≤m, and the score value can be a binary attribute value or a numerical score. Usually, the number of projects and users is very large, and the formed matrix is ​​relatively sparse. If the matrix is ​​directly used to generate recommendations, it will not be able to produce good ...

Embodiment 3

[0084] Combined with the following specific formulas, figure 2 and image 3 The scheme in embodiment 1 and 2 is introduced in detail, see the following description for details:

[0085] Two movie rating datasets, Netflix and MovieLens, are used for evaluation. Take the MovieLens data as the target data set and divide it into the training set T R and the test set T E , choose T randomly E5, 10, 20, and 30 evaluations of each user in the set are used as observation sets. In order to avoid the randomness of the experiment, in all experiments, according to the target training score set T R When generating observation scores, the experiment was randomly run 5 times and the average value was taken.

[0086] Two evaluation methods were used: mean absolute error (MAE) and root mean square error (RMSE).

[0087] M A E = Σ ( i , ...

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Abstract

The present invention discloses an item-based transfer learning recommendation method and a recommendation apparatus thereof. The transfer learning recommendation method comprises the steps of: establishing a user-item scoring matrix for an auxiliary domain and a target domain separately; standardizing the user-item scoring matrices of the auxiliary domain and the target domain; decomposing the standardized user-item scoring matrix of the auxiliary domain so as to acquire an item feature matrix; expanding the user-item scoring matrix of the target domain by use of the item feature matrix; and generating recommendation according to the expanded user-item scoring matrix of the target domain. The transfer learning recommendation apparatus comprises: an establishing module, a standardizing module, an acquiring module, an expanding module and a recommending module. According to the item-based transfer learning recommendation method and the recommendation apparatus thereof, items demanded by the user are recommended for the user more accurately and more reasonably, thereby improving the individuality and intelligence, improving user use efficiency, effectively saving user time, improving service quality of related industries, and effectively solving the problem of overload of the internet information, and the density and accuracy of user-item scoring matrices are improved.

Description

technical field [0001] The present invention relates to the fields of artificial intelligence, data mining, recommendation system, knowledge recommendation, transfer learning, etc., and in particular relates to an item-based transfer learning recommendation method. Background technique [0002] The recommendation system was first proposed by foreign scholars, and it usually includes three parts: the user information collection and preference analysis part, the recommendation algorithm part and the recommendation implementation part. The recommendation algorithm part is the core of the entire recommendation system, which can usually be divided into three types: content-based recommendation algorithm (Content-Based Recommendation Algorithm), collaborative filtering recommendation algorithm (Collaborative Filtering Recommendation Algorithm) and hybrid recommendation algorithm (Hybrid Recommendation Algorithm). Among them, the collaborative filtering recommendation algorithm is ...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/9535
Inventor 于瑞国毛洪玥喻梅王建荣于健赵满坤
Owner TIANJIN UNIV
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