Author(s)

Sun Guangmin, Yu Chenyan, Liang Xiao

Corresponding Author

Guangmin Sun

ABSTRACT

Recommender systems can be used to provide users with personalized recommendation information. They always rely on collaborating filtering (CF), since it is an effective way to establish connections between products and users. Most of the CF methods are based on neighborhood models, which calculate the similarities between users and products. Moreover, some improvements that model neighborhood relations by minimizing a cost function are made to predict a better result, since latent factor models can provide more aspects of the data, and offer more accurate results than neighborhood models. Past models were limited by using simple cosine similarity only, and they did not consider that the change of interests. Moreover, age groups may have a significant effect on the final results. In this paper, to solve the problem, a new comprehensive item similarity based on information entropy is proposed. We introduce a time and age weight to alleviate the influence on the interest change of different age groups. Some experiments were made to test the methods on the Movielens dataset, and encouraging results were obtained.

KEYWORDS

Latent factor model, K-nearest neighbor model, Information entropy, Interest change, Age group.