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Employing user attribute and item attribute to enhance the collaborative filtering recommendation.
| Content Provider | CiteSeerX |
|---|---|
| Author | Gong, Songjie |
| Abstract | Abstract—Recommender systems are web based systems that aim at predicting a customer's interest on available products and services by relying on previously rated products and dealing with the problem of information and product overload. Collaborative filtering 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, 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, the algorithms compute recommendations for the target user. 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 new collaborative filtering personalized recommendation algorithm 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 to fill the vacant ratings where necessary, and then it combines the item rating similarity and the item attribute similarity in the item based collaborative filtering process to produce recommendations. The hybrid collaborative filtering employs the user attribute and item attribute can alleviate the sparsity issue in the recommender systems. Index Terms—personalized services, collaborative filtering, rating similarity, user attribute similarity, item attribute similarity, sparsity, mean absolute error I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | User Attribute Item Attribute Collaborative Filtering Recommendation Collaborative Filtering Process Item Attribute Information User Attribute Similarity User Attribute Information Item Attribute Similarity Collaborative Filtering Target User User Rating Similarity Item Rating Similarity Product Overload Collaborative Filtering Recommendation Process Traditional Collaborative Filtering Approach Rating Information Abstract Recommender System Hybrid Collaborative Filtering User Item Rating Data Set New Collaborative Filtering Index Term Rating Similarity Sparsity Issue Similarity Value Available Product Mean Absolute Error Similar User Recommender System Popular Recommendation Technique Nowadays Algorithm Compute Recommendation Recommendation Algorithm Vacant Rating |
| Content Type | Text |
