一种基于信息老化的协同过滤推荐算法

Information Aging-based Collaborative Filtering Recommendation Algorithm

协同过滤推荐算法中存在推荐信息低时效性问题,该文针对此问题,结合信息老化理论,提出一种基于信息老化的协同过滤推荐算法。该算法利用用户的点击记录,构建项目的时效性评价模型来预测项目当前时刻被点击的概率;将模型与基于项目协同过滤推荐算法结合,综合考虑用户的兴趣和项目的时效性来发现项目的最近邻居,从而进行高时效性的推荐。实验结果表明,与传统基于项目的协同过滤推荐算法相比,该算法提高了推荐结果的时效性。

Recommendations from Collaborative Filtering （CF） recommender algorithms have low timeliness. To solve the problem, an information aging-based collaborative filtering algorithm is proposed by combining information age method. The algorithm builds a model to evaluate the timeliness of items based on users’ hit records to predict the probabilities of the items being clicked at the present time. To consider comprehensively users’ interests and the timeliness of items, the model and item-based collaborative filtering recommender algorithm are combined to find the nearest neighbor collection. Experimental results show that comparing with traditional collaborative filtering recommender algorithm the proposed algorithm can improve the timeliness of recommendations.