基于贡献因子的协同过滤推荐算法

Collaborative recommendation algorithm based on contribution factor

基于KNN邻居选择的协同过滤推荐算法在邻居选择时没有考虑邻居的盲目跟风性,导致部分邻居用户在预测目标用户对未知项目评分时的作用很小。针对这一问题,提出贡献因子,从非共同评价项目集这一角度切入,考虑邻居用户的推荐能力,计算邻居用户的推荐贡献度,结合传统的用户间相似度共同进行邻居选择,并重新计算邻居用户预测未知项目的权重,提升推荐性能。实验结果表明,本改进算法提高了推荐准确度。

A collaborative recommendation algorithm based on KNN didn＇ t take in count the neighbours that blindly followed others, ideas,which made the neighbours useless to predicte the rates of the target users to the unknown items. To address this problem, this paper put forward the contribution factor, thought over the non-common evaluation items, considered the contribu- tion of neighbour recommendation, and selected the neigbbours with the traditional similarity. And it recalculated the neighhour prediction weights to the unknown projects to advance the performance of recommendation. The experiments show that this algorithm improves the degree of accuracy.