摘要
在协同过滤推荐系统中,商品被视为特征,用户提供他们对购买的商品的评分。通过对用户评分的学习,推荐系统可以向用户推荐他们可能需要的产品。然而电子商务通常有相当多的产品,如果在推荐前要对每一个商品都进行考虑,推荐系统将是非常低效的。提出一种改进的ItemRank方法,应用自构建聚类算法来减少商品数量相关的维度,然后直接在聚类上运行推荐算法。最后,对推荐聚类进行变换得到推荐商品列表推荐给不同的用户。所提出的方法在计算推荐商品时所需的时间大大减少。实验结果表明,在不影响推荐质量的前提下,推荐系统的效率得到了提高。
In collaborative filtering recommender systems, products are regarded as features and users are requested to provide ratings to the prod- ucts they have purchased. By learning from the ratings, such a recommender system can recommend interesting products to users. Howev- er, there are usually quite a lot of products involved in E-commerce and it would be very inefficient if every product needs to be consid- ered before making recommendations. Proposes an improved approach based ItemRank which applies a self-constructing clustering algo- rithm to reduce the dimensionality related to the number of products, Recommendation is then done with the clusters. Finally, re-transfor- mation is performed and a ranked list of recommended products is offered to each user. With the proposed approach, the processing time for making recommendations is much reduced. Experimental results show that the efficiency of the recommender system can be improved without compromising the recommendation quality.
