基于用户兴趣传播的协同过滤方法

推荐系统帮助用户过滤无用信息并预测其可能感兴趣的产品。在推荐系统中,协同过滤是应用最为广泛的方法之一。然而,传统的协同过滤方法是在产品维度上计算用户相似度,而且在计算相似度时无法考虑邻居用户的影响。因此,该类方法往往受到高维度、数据稀疏等问题的困扰。为此,本文提出一种基于用户兴趣传播的协同过滤方法,在兴趣维度上计算用户相似度,同时考虑了兴趣在不同用户间的传播。该方法不仅可以有效防止冷启动和数据稀疏问题,而且具有较高的预测准确度。在标准数据集MovieLens上的测试结果表明了本文算法的有效性。

User Interests Transmission Based Collaborative Filtering Approach

Recommender systems help users filter useless information and predict the products users may like.Collaborative filtering is one of the most widely used approaches in recommender systems.However,the traditional collaborative filtering methods compute users' similarities in the dimension of products,and they do not take the influence of neighbor users into consideration when computing such similarities.Thus,they often suffer from the problems such as high dimensionality and data sparseness.To that end,we pro...