Method by using time factors in recommender system
FAN Jiabing1,2, WANG Peng3, ZHOU Weibo1,2, YAN Jingjing1,2
1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu Sichuan 610041, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. College of Software Engineering, Chengdu University of Information Technology, Chengdu Sichuan 610225, China
Concerning the problem that traditional recommendation algorithm ignores the time factors, according to the similarity of individuals' short-term behavior, a calculation method of item correlation by using time decay function based on users' interest was proposed. And based on this method, a new item similarity was proposed. At the same time, the TItemRank algorithm was proposed which is an improved ItemRank algorithm by combining with the user interest-based item correlation. The experimental results show that: the improved algorithms have better recommendation effects than classical ones when the recommendation list is small. Especially, when the recommendation list has 20 items, the precision of user interest-based item similarity is 21.9% higher than Cosin similarity and 6.7% higher than Jaccard similarity. Meanwhile, when the recommendation list has 5 items, the precision of TItemRank is 2.9% higher than ItemRank.
范家兵, 王鹏, 周渭博, 燕京京. 在推荐系统中利用时间因素的方法[J]. 计算机应用, 2015, 35(5): 1324-1327.
FAN Jiabing, WANG Peng, ZHOU Weibo, YAN Jingjing. Method by using time factors in recommender system. Journal of Computer Applications, 2015, 35(5): 1324-1327.
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2015, Vol. 35 
