改进的聚类邻居协同过滤推荐算法

An Adaptive Algorithm of Collaborative Filtering Recommender Based on Aggregate-Neighborhood

推荐系统运用统计和知识发现技术在实时交互系统中提供产品推荐,并且已经在电子商务中取得了较广泛的应用。本文中我们介绍了一种不同于以往的推荐产生算法,称之为改进的聚类邻居协同过滤推荐算法,试验表明我们的算法比k-邻近点算法和聚类邻居算法具有更好的效果。

Recommender systems apply statistical and knowledge discovery techniques to the problem of making prod-uct recommendations during a live interaction and they are achieving widespread success in E-commerce nowadays . In this paper,we introduce a different recommendation generation algorithm, we name it adaptive-aggregate-neighbor-hood collaborative filtering algorithm. Our experiments suggest that we prove that our algorithm has better perfor-mance than k-nearest neighbor and aggregate-neighborhood approach .