关键词: 推荐系统, 协同过滤, 自然最近邻用户, 无参, 推荐准确率

Abstract: Current neighborhood-based recommendation approaches such as KNN（K-Nearest Neighbor） and K-means collaborative filtering are commonly utilized in predicting user ratings. However, for these methods, the total number K of neighbors is difficult to determine and the recommendation accuracy is limited, which may lead to undesired recommendation effects. Starting from choosing the nearest neighbors, this paper proposes a Collaborative Filtering recommendation integrating user-centric Natural Nearest Neighbor（CF3N） algorithm that self-adaptively searches target user’s natural nearest neighbor set without the parameter K. Then the natural nearest neighbor set is combined with the active nearest neighbor set into an integrated neighbor set, which is exploited to predict target user’s ratings. Experiments are performed to compare the CF3N algorithm with CF-KNN and INS-CF algorithms using MovieLens datasets with the Mean Absolute Error（MAE） and the Root Mean Square Error（RMSE） as evaluation indexes. The results show that the CF3N algorithm significantly improves the recommendation accuracy, thus providing a new approach to predicting user ratings in movies.

Key words: recommender system, collaborative filtering, natural nearest neighbor, parameter-free, recommendation accuracy