面向电视观众的用户兴趣偏好建模方法
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- 英文论文题名：An Approach of Building User Interest Preference Model tor TV Viewers
- 论文作者：罗贺 ; 赵培
- 英文论文作者：LUO He ; ZHAO Pei ; School of Management ; Hefei University of Technology ; Key Laboratory of the Ministry of Education on Process Optimization & Intelligent Decision Making
- 年：2016
- 作者机构：合肥工业大学管理学院;过程优化与智能决策教育部重点实验室;
- 论文关键词：电视节目推荐 ; 观看行为 ; 用户兴趣建模
- 英文论文关键词：TV program recommendation ; viewing behavior ; user interest preference model
- 会议召开时间：2016-11-12
- 会议录名称：第十八届中国管理科学学术年会论文集
- 英文会议录名称：Proceedings of the 18th Chinese Annual Conference on Management Science
- 语种：中文
- 分类号：C93
- 学会代码：ZHYJ
- 会议名称：第十八届中国管理科学学术年会
- 会议地点：中国陕西西安
- 主办单位：中国优选法统筹法与经济数学研究会、西安交通大学、中国科学院科技战略咨询研究院、《中国管理科学》编辑部
- 学会名称：中国优选法统筹法与经济数学研究会
- 页数：6
- 文件大小：625k
- 原文格式：O
- 会议级别：全国
摘要
在电视节目的个性化推荐过程中,针对用户偏好难以显式获取的问题,本文提出了一种基于观看行为的用户兴趣偏好建模方法。该方法将用户观看电视节目过程中的点播操作和观看时长与电视节目的基本属性相结合,从时间和频率两个维度描述用户对电视节目的偏好程度,并建立用户兴趣偏好矩阵。在此基础上,针对2602个用户在7天内的116010条点播记录,采用top-N推荐算法进行实验。结果表明,该方法能够有效地描述用户兴趣偏好,提高电视节目推荐的准确率和召回率。
Aiming at the issue of hard to obtain user explicit preferences in Personalized TV program recommendation,an approach of building user interest preference model is proposed based on user viewing behavior.This approach not only uses the operation and duration in the process of viewing TV programs,but also combines with the basic properties of TV programs,then user interest preference is described from two dimensions of time and frequency,and the interest preference matrix is established.Our approach is measured by top-N algorithm on 116010 historical data of 2602 users watched TV programs during 7 days.The experimental results show that the proposed approach can describe user interest preference accurately and perform well in precision and recall.
Aiming at the issue of hard to obtain user explicit preferences in Personalized TV program recommendation,an approach of building user interest preference model is proposed based on user viewing behavior.This approach not only uses the operation and duration in the process of viewing TV programs,but also combines with the basic properties of TV programs,then user interest preference is described from two dimensions of time and frequency,and the interest preference matrix is established.Our approach is measured by top-N algorithm on 116010 historical data of 2602 users watched TV programs during 7 days.The experimental results show that the proposed approach can describe user interest preference accurately and perform well in precision and recall.
引文
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[13]何跃,马丽霞,腾格尔.基于用户访问兴趣的Web日志挖掘[J].系统工程理论与实践,2012,32(06):1353-1361.
[14]朱志国.基于隐马尔可夫链模型的电子商务用户兴趣导航模式发现[J].中国管理科学,2014,22(04):67-73.
[15]Oh J,Kim S.Kim J,et al.When to recommend:A new issue on TV show recommendation[J].Information Sciences,2014,280:261-274.
[16]Zhu Yi,He Li,Wang Xiaojun.User interest modeling and self-adaptive update using relevance feedback technology[J].Procedia Engineering,2012,29(4):721-725.
[17]Shin H.Lee M,Kim E Y.Personalized digital TV content recommendation with integration of user behavior profiling and multimodal content rating[J].IEEE Transactions on Consumer Electronics,2009,55(3):1417-1423.
[18]Sanchez F.Barrilero M,Alvarez F,et al.User interest modeling for social TV-recommender systems based on audiovisual consumption[J].Multimedia Systems,2013,19(6):493-507.
[19]Kim E,Pyo S.Park E,et al.An automatic recommendation scheme of TV program contents for(IP)TV personalization[J].IEEE Transactions on Broadcasting,2011,57(3):674-684.
[20]Fan Na,Yang Yan,He Liang.An algorithm of users access patterns mining based on video recommendation[A]//park J,JinQ,Sang-oo YM,et al.Human centric technology and service in smart space.Dordrecht:Springer,2012:37-42
[21]Pyo S.Kim E.Kim M.Automatic and personalized recommendation of TV program contents using sequential pattern mining for smart TV user interaction[J].Multimedia Systems,2013,19(6):527-542.
[22]Kassak O,Kompan M,Bielikova M.User preference modeling by global and individual weights for personalized recommendation[J].Acta Polytechnica Hungarica,2015,12(8):27-41.
[23]梁昌勇,冷亚军,王勇胜,等.电子商务推荐系统中群体用户推荐问题研究[J].中国管理科学,2013,21(03):153-158.
[24]Bobadilla J,Hernando A,Ortega F,et al.Collaborative filtering based on significances[J].Information Sciences,2012,185(1):1-17.
[2]2015年度电视综艺节目概览分析[EB/OL].[2016-01-26].blog.sina.com.cn/s/blog_a35496f50102wfsk.html
[3]Park D H,Kim H K,Choi I Y,et al.A literature review and classification of recommender systems research[J].Expert Systems with Applications,2012,39(11):10059-10072.
[4]Bobadilla J,Ortega F,Hernando A,et al.Recommender systems survey[J].Knowledge-Based Systems,2013,46(1):109-132.
[5]Chang Na,Irvan M.Terano T.A TV program recommender framework[J].Procedia Computer Science,2013,22:561-570.
[6]Prota T.Bispo A,Prud■ncio R,et al.A literature review of recommender systems in the television domain[J].Expert Systems with Applications,2015,42(22):9046-9076.
[7]吴丽花,刘鲁.个性化推荐系统用户建模技术综述[J].情报学报,2006,25(1):55-62.
[8]Zenebe A,Zhou L.Norcio A F.User preferences discovery using fuzzy models[J].Fuzzy Sets&Systems,2010,161(23):3044-3063.
[9]Martinez A B B,Arias J J P,Vilas A F,et al.What's on TV tonight?An efficient and effective personalized recommender system of TV programs[J].IEEE Transactions on Consumer Electronics,2009,55(1):1-2.
[10]印鉴,王智圣,李琪.基于大规模隐式反馈的个性化推荐[J].软件学报,2014,25(9):1953-1966.
[11]Pazzani M,Billsus D.Learning and revising user profiles:The identification of interesting web sites[J].Machine learning,1997,27(3):313-331.
[12]Adomavicius G.Tuzhilin A.Toward the next generation of recommender systems:A survey of the state-of-theart and possible extensions[J].IEEE transactions on knowledge and data engineering,2005,17(6):734-749.
[13]何跃,马丽霞,腾格尔.基于用户访问兴趣的Web日志挖掘[J].系统工程理论与实践,2012,32(06):1353-1361.
[14]朱志国.基于隐马尔可夫链模型的电子商务用户兴趣导航模式发现[J].中国管理科学,2014,22(04):67-73.
[15]Oh J,Kim S.Kim J,et al.When to recommend:A new issue on TV show recommendation[J].Information Sciences,2014,280:261-274.
[16]Zhu Yi,He Li,Wang Xiaojun.User interest modeling and self-adaptive update using relevance feedback technology[J].Procedia Engineering,2012,29(4):721-725.
[17]Shin H.Lee M,Kim E Y.Personalized digital TV content recommendation with integration of user behavior profiling and multimodal content rating[J].IEEE Transactions on Consumer Electronics,2009,55(3):1417-1423.
[18]Sanchez F.Barrilero M,Alvarez F,et al.User interest modeling for social TV-recommender systems based on audiovisual consumption[J].Multimedia Systems,2013,19(6):493-507.
[19]Kim E,Pyo S.Park E,et al.An automatic recommendation scheme of TV program contents for(IP)TV personalization[J].IEEE Transactions on Broadcasting,2011,57(3):674-684.
[20]Fan Na,Yang Yan,He Liang.An algorithm of users access patterns mining based on video recommendation[A]//park J,JinQ,Sang-oo YM,et al.Human centric technology and service in smart space.Dordrecht:Springer,2012:37-42
[21]Pyo S.Kim E.Kim M.Automatic and personalized recommendation of TV program contents using sequential pattern mining for smart TV user interaction[J].Multimedia Systems,2013,19(6):527-542.
[22]Kassak O,Kompan M,Bielikova M.User preference modeling by global and individual weights for personalized recommendation[J].Acta Polytechnica Hungarica,2015,12(8):27-41.
[23]梁昌勇,冷亚军,王勇胜,等.电子商务推荐系统中群体用户推荐问题研究[J].中国管理科学,2013,21(03):153-158.
[24]Bobadilla J,Hernando A,Ortega F,et al.Collaborative filtering based on significances[J].Information Sciences,2012,185(1):1-17.