Abstract-Recommender systems are being widely applied in many fields, such as e-commerce etc, to provide products, services and information to potential customers. Collaborative filtering as the most successful approach, which recommends contents to the current customers mainly is based on the past transactions and feedback of the similar customer. However, it is difficult to distinguish the similar interests between customers because the sparsity problem is caused by the insufficient number of the transactions and feedback data, which confined the usability of the collaborative filtering. This paper proposed the direct similarity and the indirect similarity between users, and computed the similarity matrix through the relative distance between the user's rating; using association retrieval technology to explore the transitive associations based on the user's feedback data, realized a new collaborative filtering approach to alleviate the sparsity problem and improved the quality of the recommendation. In the end, we implemented experiment based on Movielens data set, the experiment results indicated that the proposed approach can effectively alleviate the sparsity problem, have good coverage rate and recommendation quality.
Using Associative retrieval technology to explore the transitive associations based on the user's feedback data, realized a new collaborative filtering approach to alleviate the sparsity problem and improved the quality of the RS REF .
Solving the Sparsity Problem in Recommender Systems Using Association Retrieval
JCP
JCP
Computer Science