Method by using time factors in recommender system

doi:10.11772/j.issn.1001-9081.2015.05.1324

Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (5): 1324-1327.DOI: 10.11772/j.issn.1001-9081.2015.05.1324

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Method by using time factors in recommender system

FAN Jiabing^1,2, WANG Peng³, ZHOU Weibo^1,2, YAN Jingjing^1,2

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

Received:2014-12-02 Revised:2015-01-13 Online:2015-05-14 Published:2015-05-10

在推荐系统中利用时间因素的方法

范家兵^1,2, 王鹏³, 周渭博^1,2, 燕京京^1,2

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

通讯作者: 范家兵
作者简介:范家兵(1990-),男,安徽六安人,硕士研究生,主要研究方向:个性化推荐; 王鹏(1975-),男,四川犍为人,教授,博士,CCF会员,主要研究方向:云计算、并行计算、量子算法; 周渭博(1981-),男,山东烟台人,博士研究生,主要研究方向:大数据智能处理; 燕京京(1989-),女,河南安阳人,硕士研究生,主要研究方向:数据挖掘.
基金资助:
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).

Abstract

Abstract:

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.

Key words: collaborative filtering, item correlation, item similarity, interest in attenuation, ItemRank, graph model, Ebbinghaus curve

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%.

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

CLC Number:

TP182

FAN Jiabing, WANG Peng, ZHOU Weibo, YAN Jingjing. Method by using time factors in recommender system[J]. Journal of Computer Applications, 2015, 35(5): 1324-1327.

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.

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Method by using time factors in recommender system

在推荐系统中利用时间因素的方法

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