Project recommendation method based on attribute coupled matrix decomposition

Abstract

The invention discloses a project recommendation method based on attribute coupled matrix decomposition. The method comprises steps as follows: firstly, giving attribute information of projects and calculating the similarity between the projects with a coupled object similarity measuring index; then learning conceal eigenvectors of users and the projects with a matrix decomposition algorithm, during learning of the conceal eigenvectors of the projects, constructing a regularized term by means of the attribute information of the projects, and constraining the execution process of matrix decomposition, so that the projects with similar attribute information have similar conceal eigenvectors; finally, according to the learned conceal eigenvectors of the users and the projects, projecting scores of projects which are not scored by the users by use of inner products of the conceal eigenvectors of the users and the predicts, and providing personalized project recommendation for the users according to the predicted scores. The problems of similarity calculation of the projects, cold start of project terminals and recommendation accuracy in a recommendation system are solved with the method.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to an item recommendation method based on attribute coupling matrix decomposition. Background technique [0002] With the continuous development of Internet technology, it is becoming more and more difficult to find valuable relevant information from massive data, and users are faced with a serious problem of information overload. The recommendation system analyzes the user's historical activity data, mines the user's potential preferences, and provides users with personalized recommendation services. It has become an effective means to solve the problem of information overload, and has attracted extensive attention from academia and industry in recent years. Typical applications of recommendation systems include product recommendations from Amazon and Taobao, movie recommendations from Netflix, music recommendations from Last.fm, friend recommendations from LinkedIn, news recom...

AI Technical Summary

Problems solved by technology

However, item feature representations learned by probabilistic topic models may not be concise and effective, and inconsistent with the basic assumption of matrix factorization (only a small number of hidden factors affect users' preferences and characterize items)

Moreover, it is difficult to deal with the randomness of user comment information, so that the similarity between users and items cannot be well described

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