在推荐系统中利用时间因素的方法  

Method by using time factors in recommender system

作　　者：范家兵[1,2] 王鹏[3] 周渭博 燕京京 

机构地区：[1]中国科学院成都计算机应用研究所,成都610041 [2]中国科学院大学,北京100049 [3]成都信息工程学院软件工程学院,成都610225

出　　处：《计算机应用》2015年第5期1324-1327,1378,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60702075);广东省科技厅高新技术产业化科技攻关项目(2011B010200007);四川省青年科学基金资助项目(09ZQ026-068);成都市科技局创新发展战略研究项目(11RXYB016ZF);四川省科技创新苗子工程项目(2014-063)

摘　　要：针对传统推荐算法忽略时间因素的问题,根据个体用户短期行为的相似性,利用时间衰减函数计算项目间相关关系,提出基于用户兴趣的项目关联度;将其用于项目相似度的计算,提出基于用户兴趣的项目相似度;同时基于项目关联度对ItemRank算法进行改进,提出一种结合时间因素的TItemRank算法。实验结果表明,利用项目关联度对推荐算法进行改进时,在推荐项目数较少的情况下能够明显地改善推荐效果。特别地,在推荐项目数为20时,基于用户兴趣的项目相似度相比余弦相似度和Jaccard相似度,推荐准确率分别提高了21.9%、6.7%;在推荐项目数为5时,TItemRank算法相比ItemRank算法推荐准确率提高2.9%。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.

关 键 词：协同过滤 项目关联度 项目相似度 兴趣衰减 ItemRank 图模型 艾宾浩斯曲线 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]