基于专家信任的协同过滤推荐算法改进研究

针对目前协同过滤推荐算法存在的冷启动、数据稀疏、可扩展性不高以及未考虑到不同社区簇之间可能存在相关性导致的推荐准确度低的问题,提出了一种在考虑同社区簇内专家信任基础上结合不同社区簇专家信任的推荐算法。在改进相似度计算时,改进算法不仅结合了Jaccard相关系数、用户的平均评分因子以及加权处理的Pearson相关系数,还结合了用来惩罚热门物品权重的流行度。在改进评分预测时,改进算法在引入了传统聚类推荐算法中的同社区簇专家信任后,还引入了不同社区簇专家信任。实验在MovieLens数据集上进行,实验结果表明,改进算法不仅缓解了冷启动和数据稀疏等问题,还显著提高了推荐准确度。

An improved collaborative filtering recommendation algorithm based on expert trust

Aiming at the problems of cold start, sparse data, low scalability and low recommendation accuracy caused by insufficient consideration of the correlation between different community clusters, we propose a recommendation algorithm based on the trust of experts in the same community cluster and the trust of experts in different community clusters. In improving the similarity calculation, the improved algorithm not only combines Jaccard correlation coefficient, average score factor of users and Pearson correlation coefficient of weighted processing, but also combines the popularity used to punish the proportion of hot items. When improving the score prediction, the improved algorithm introduces the trust of experts in the same community cluster in the traditional clustering recommendation algorithm, and also introduces the trust of experts in different community clusters. Experiments on the MovieLens dataset show that the improved algorithm not only alleviates the problems of cold start and data sparseness, but also significantly improves recommendation accuracy.