A Collaborative Recommender Combining Item Rating Similarity and Item Attribute Similarity

Collaborative filtering (CF) is the most popular recommendation technique nowadays. Traditional CF approaches compute a similarity value between the target user and each other user by computing the relativity of their rating style, which is the set of ratings given on the same items. Based on the ratings of the most similar users, commonly referred to as neighbors, CF algorithms compute recommendations for the target user. The problem with this approach is that the similarity value is only considering the user-item ratings. To solve this problem, this paper combining the item attribute similarity and the item rating similarity, which takes into account the influence of item information and user rating to enhance the item-based CF. The experimental results show that the algorithm combined the item attribute similarity and the item rating similarity is promising, since it does not only solve the dataset sparsity problem of recommender systems, but also assists in increasing the accuracy of systems employing it.