A Collaborative Recommender Based on User Information and Item Information

Collaborative recommender is the most popular recommendation technique nowadays and it mainly employs the user item rating data set. Traditional collaborative filtering approaches compute a similarity value between the target user and each other user by computing the relativity of their ratings, and they only consider the ratings information. User attribute information associated with a user's personality and item attribute information associated with an item's inside are rarely considered in the collaborative filtering recommendation process. In this paper, a hybrid collaborative filtering recommender is proposed which employs the user attribute information and the item attribute information. This approach combines the user rating similarity and the user attribute similarity in the user based collaborative filtering process and then it combines the item rating similarity and the item attribute similarity in the item based collaborative filtering process to produce recommendations. The collaborative filtering recommender employs the user attribute and item attribute can alleviate the sparsity issue in the recommender systems.