关键词: 最近邻居搜索, 协同过滤, 推荐算法, 邻居利用率, 线上推荐

Abstract:

To sovle the time-consuming problem of searching nearest neighbor in collaborative filtering and the problem that neighbor information are not effectively utilized in the prediction calculation, a method for fast searching nearest neighbors is proposed. The proposed method changes the mode of organizing data in the rating matrix and constructs two kinds of lists：the user rating list of items and the item rating list of users. According to these two lists, users or items that have an impact on the predictive rating values are filtered out. Then, the neighbor set of the target user or target item are determined. In the proposed method, unnecessary similarity calculations are eliminated, which improves computational efficiency. Moreover, the proposed method also effectively guarantees the neighbor utilization rate in prediction calculations and improves recommendation quality. The experimental results in the Movilens100k dataset and Movielens1M dataset show that the proposed method greatly improves the performance of collaborative filtering, such as running time, MAE, RMSE and F1 value. Therefore, the proposed method has good application value in the field of recommendation systems.

Key words: nearest-neighbor searching, collaborative filtering, recommendation algorithm, neighbor utilization, online recommendation