Probabilistic matrix factorization(PMF) model realizing fusion of similarity and common rating item quantity

Abstract

The invention discloses a probabilistic matrix factorization(PMF) model realizing fusion of similarity and common rating item quantity and provides a novel probabilistic matrix factorization model capable of fusing similarity and common rating item quantity. According to the model based on the PMF, the inner product of the factor matrix is restrained by the similarity, an appropriate penalty function is built to the common rating item quantity, the penalty function and the inner product of the factor matrix are associated to realize co-constraint to the factor matrix, and thus, quality of the factor matrix is improved. The advantage of the similarity in terms of exploring local relation and the advantage of the PME model in terms of exploring global relation are combined successfully; besides, according to the model, the influence from the common rating item quantity on trustworthiness of the similarity is also taken into consideration. Compared with the previous research, the model has the advantage of higher precision.

Description

technical field [0001] The present invention relates to the field of recommendation algorithms, in particular to a probabilistic matrix decomposition model that combines similarity and the number of common scoring items, mainly combining similarity and the number of common scoring items in the traditional probability matrix factorization model (Probabilistic Matrix Factorization, PMF) , so as to alleviate data sparsity and improve recommendation accuracy. Background technique [0002] With the development of Internet technology, the user-centered information production mode has caused the explosive growth of Internet information, and people are facing more and more serious "information overload" problems. The recommendation system can mine user preferences and recommend items they like (movies, books, music, commodities, etc.) to users, thereby providing personalized recommendation services and effectively alleviating the problem of "information overload". The recommendatio...

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Problems solved by technology

[0004] The memory-based collaborative filtering recommendation algorithm has a high local learning ability and can recommend items with high novelty. The disadvantage is that it cannot deal with the problem of data sparsity; while PMF has high scalability, strong global learning ability, and can Handle data sparsity better

In order to combine the advantages of the two, many scholars are committed to integrating similarity in PMF. Although the recommendation accuracy is improved to a certain extent, they ignore the impact of the number of common scoring items on the recommendation results.

Related studies have shown that the number of common rating items between users (items) can reflect the trustworthiness of their similarity, which can affect the recommendation effect

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