A Preference Prediction Method Based on the Optimization of Basic Similarity Space Distribution

The similarity measure methods of preference behavior in the existing collaborative filtering based recommender systems are unable to acquire the real nearest neighbors, which have influenced the prediction accuracy. To solve this problem, an users’ preference prediction method based on the optimization of basic similarity space distribution is proposed. In the beginning, this method uses cosine similarity, constrained cosine similarity and Pearson correlation coefficient to get the original similarities among users. Secondly, it generates the preference center based on the distribution characteristic of users’ preference similarity, and then it get the average similarity range based on the behavior distance between other preference behavior and preference center to build the basic similarity space. Finally, the method generates the modified model based on average nearest neighbors and abnormal ratings to optimize the basic similarity space, and basing on which generate predictions for users. The authors present empirical experiments by using a real extensive data set. Experimental results show that the proposed method can achieve lower MAE about 12.8% and 9.7% compared with WSCF and OTCF, and the coverage rate is increased about 5.79% and 3.83%, and the diversity is the same with WSCF and is increased about 4.3% compared with OTCF, which indicates that the proposed method can efficiently improve recommendation quality.