A Collaborative Filtering Algorithm of Calculating Similarity Based on Item Rating and Attributes

Abstract

Nowadays, the collaborative filtering techniques have demonstrated an excellent performance in the top-N recommendation. However conventional methods in similarity measurement are insufficient when the condition of data sparsity and cold start occur, which leads to a poor accuracy in prediction. In order to concur the limitation, a collaborative filtering algorithm of calculating similarity based on item rating and attributes is proposed. Firstly, we calculate the similarity of item attributes, then calculate the similarity of the project according to the user rating of the project. Meanwhile, a weighted control coefficient is proposed to combine the similarity between item attributes and rating of items, which contribute to obtain nearest neighbors. Experiments have shown that our algorithm has major potential in solving the problem of cold start, therefore improving the precision of the recommendation system.