User Similarity Adjustment for Improved Recommendations

Recommender systems are becoming more and more attractive in both research and commercial communities due to Information overload problem and the popularity of the Internet applications. Collaborative Filtering, a popular branch of recommendation approaches, makes predictions based on historical data available in the system. In particular, user based Collaborative Filtering largely depends on how users rate various items of the database and the success of such a system largely relies on pair wise similarity between users. However popular items may give a negative effect on choosing similar users of the target user. The proposed work namely User Similarity Adjustment based on Item Diversity USA_ID is designed to achieve personalized recommendations by modifying user similarity scores, for the purpose of reducing the negative effects of popular items in user based Collaborative Filtering framework. A Recommender system is focusing exclusively on achieving accurate recommendations i.e., providing the most relevant items for the needs of a user. From user's perspective, they would not be interested when they are facing monotonous recommendations even if they are accurate. Whilst much research effort is spent on improving accuracy of recommendations, less effort is taken on analyzing usefulness of recommendations. Novelty and Diversity have been identified as key dimensions of recommendation utility. It has been made clear that greater accuracy leads to lower diversity which results in accuracy-diversity trade off in personalized recommender systems. The proposed work provides an approach to increase the utility of a Recommender system by improving accuracy as well as diversity. Experiments are conducted on the bench mark data set MovieLens and the results show efficiency of the proposed approach in improving quality of predictions.