%0 Journal Article %A WEI Dingfeng %A LI Liang %A CHAI Jing %T Social Recommendation Algorithm by Fusing Item Information %D 2021 %R 10.3778/j.issn.1002-8331.2006-0012 %J Computer Engineering and Applications %P 198-204 %V 57 %N 19 %X

Most social recommendation algorithms only constrain the user’s feature vector but not the item’s feature vector. Aiming at this problem, a social recommendation algorithm by fusing item information is proposed. The model firstly constructs an item similarity network based on the user-item interaction diagram. Based on this, random walks and SkipGram’s method are used to construct an item similarity network, and learns the item similarity network, social network and the user-item interaction diagram through the graph neural network to obtain the feature vectors of the users and items coding, and finally on the basis of matrix decomposition, further constraints on the feature vectors of the user and items at the same time, and the iterative update method is used to obtain the final feature vectors of the users and items. In order to verify the performance of the recommendation algorithm, experiments are performed on the FilmTrust, Ciao and Douban datasets. The experimental results show that the proposed ISGCF algorithm has a better recommendation effect than the classic recommendation algorithm, and effectively alleviates the cold start problem.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2006-0012