基于时间加权的个性化推荐算法研究

Research on the Personalized Recommendation Algorithm Based on Time Weight

协同过滤算法是个性化推荐系统中应用最成功的推荐算法之一,但传统的算法没有考虑在不同时间段内寻找最近邻居问题,导致寻找的邻居集合可能不是最近邻居集合。针对这个问题,本文提出了基于时间加权的协同过滤算法。该算法赋予每项评分一个按时间逐步递减的权重,利用加权后的评分寻找目标用户的最近邻居。实验表明,改进的算法提高了协同过滤推荐系统的推荐质量。

Collaborative filtering algorithm is one of the most successful technology for building a personalized recommendation system. But the traditional CF algorithm does not consider finding the nearest neighbors in different time periods, leading to the fact that neighbors may not be the nearest neighbor set. For this reason, a CF algorithm based on time weight is proposed. The rating is given a weight by a gradual time decrease which is weighted to search the nearest neighbor of the target user. The experimental results show that the presented algorithm can improve recommendation quality of the collaborative filtering recommendation algorithm.