关键词: 协同过滤, 推荐系统, k近邻, 数据挖掘, 知识发现

Abstract: Collaborative filtering is one of widely-used techniques in recommendation systems. Data sparsity is a main factor which affects the prediction accuracy of collaborative filtering. SlopeOne algorithm uses linear regression model to solve data sparsity problem. k-nearest-neighborhood method based on users similarities can optimize the quality of ratings made by users participating in prediction. Based on SlopeOne algorithm, this paper presents a new collaborative filtering algorithm combining dynamic k-nearest-neighborhood and SlopeOne. Firstly, different numbers of neighbors for each user are dynamically selected according to the similarities with other users. Secondly, average deviations between pairs of relevant items are generated on the basis of ratings from neighbor users. At last, the object ratings are predicted by linear regression model. Experiments on the MovieLens dataset show that the proposed algorithm gives better recommendations and is more robust to data sparsity than SlopeOne. It also outper-forms other collaborative filtering algorithms on prediction accuracy.

Key words: collaborative filtering, recommendation system, k-nearest-neighborhood, data mining, knowledge discovery