A Graph Model for Recommender Systems
Hong Chen, Mingxin Gan, Mengzhao Song · Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) · 2013
With the constant enlargement of the scope and coverage of the Internet, the traditional search algorithms just help to filter data, without considering the needs of individuals.Therefore, various recommender systems employing different data representations and recommendation methods are currently used to cope with these challenges.In this paper, inspired by the network-based user-item rating matrix, we introduce an improved algorithm which combines the similarity of items with a dynamic resource allocation process.To demonstrate its accuracy and usefulness, this paper compares the proposed algorithm with collaborative filtering algorithm using data from MovieLens, and finally verifies the results.The evaluation shows that, the improved recommendation algorithm based on graph model achieves more accurate predictions and more reasonable recommendation than collaborative filtering algorithm or the basic graph model algorithm does.Meanwhile, the algorithm can effectively mitigate the sparse of the rating matrix.