计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 415-422.

• 大数据与数据挖掘 • 上一篇    下一篇

基于标签信息特征相似性的协同过滤个性化推荐

何明,要凯升,杨芃,张久伶   

  1. 北京工业大学信息学部 北京100124
  • 出版日期:2018-06-20 发布日期:2018-08-03
  • 作者简介:何 明(1975-),男,博士,副教授,主要研究方向为推荐系统、机器学习,E-mail:heming@bjut.edu.cn;要凯升(1994-),男,硕士生,主要研究方向为推荐系统、数据挖掘;杨 芃(1994-),男,硕士生,主要研究方向为数据挖掘;张久伶(1990-),男,硕士生,主要研究方向为机器学习。
  • 基金资助:
    国家自然科学基金项目(91646201,91546111),北京市教委科研计划一般项目(KM201710005023)资助

Collaborative Filtering Personalized Recommendation Based on Similarity of Tag Information Feature

HE Ming, YAO Kai-sheng,YANG Peng,ZHANG Jiu-ling   

  1. Faulty of Information Technology,Beijing University of Technology,Beijing 100124,China
  • Online:2018-06-20 Published:2018-08-03

摘要: 标签推荐系统旨在利用标签数据为用户提供个性化推荐。已有的基于标签的推荐方法往往忽视了用户和资源本身的特征,而且在相似性度量时仅针对项目相似性或用户相似性进行计算,并未充分考虑二者之间的有效融合,推荐结果的准确性较低。为了解决上述问题,将标签信息融入到结合用户相似性和项目相似性的协同过滤中,提出融合标签特征与相似性的协同过滤个性化推荐方法。该方法在充分考虑用户、项目以及标签信息的基础上,利用二维矩阵来定义用户-标签以及标签-项目之间的行为。构建用户和项目的标签特征表示,通过基于标签特征的相似性度量方法计算用户相似性和项目相似性。基于用户标签行为和用户与项目的相似性线性组合来预测用户对项目的偏好值,并根据预测偏好值排序,生成最终的推荐列表。在Last.fm数据集上的实验结果表明,该方法能够提高推荐的准确度,满足用户的个性化需求。

关键词: 协同过滤, 标签, 推荐系统, 相似性计算

Abstract: Tag recommendation systems are aimed to provide personalized recommendation using tag data for users.Previous tag based recommendation methods usually neglect the characteristics of users and items,and similarity mea-sures are unconsidered fully incorporating effectively both user similarity and item similarity,which leads to deviation of recommendation results.To address this issue,this paper proposed the collaborative filtering recommendation method of combining tag features and similarity for personalized recommendation.Two-dimensional matrix is used to define actions among user-tag and tag-item based on integrating information among users,tags and items.Tag features representation is constructed,and user similarity and item similarity are calculated by similarity measure method based on tag features.The user preferences for items are predicted by their tag behaviors and linear combination of similarity of users and items,and the recommended list is generated according to the rank of preferences.The experimental results on Last.fm show that the proposed method can improve recommendation accuracy and satisfy the requirement for users.

Key words: Collaborative filtering, Tag, Recommendation systems, Similarity computation

中图分类号: 

  • TP391
[1]ADOMAVICIUS G,TUZHILIN A.Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions[C]∥Proceedings of the IEEE Transactions Knowledge and Data Engineering,2005,17(6):734-749.
[2]KATARYA R,VERMA O P.Privacy-Preserving and Secure Recommender System Enhance with K-NN and Social Tagging[C]∥2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud).New York,2017:52-57.
[3]HUANG L W,LI D Y.A review of information recommendation in social media[J].CAAI Trans.on Intelligent Systems,2012,7(1):1-8.
[4]SU X,KHOSHGOFTAAR T M.A survey of collaborative filtering techniques [J].Advances in Artificial Intelligence,2009,2009(12):4.
[5]ORTEGA F,HERNANDO A,BOBADILLA J,et al.Recom- mending items to group of users using Matrix Factorization based Collaborative Filtering[J].Information Sciences,2016,345(C):313-324.
[6]LIN J,SUGIYAMA K,KAN M Y,et al.Addressing cold-start in app recommendation:latent user models constructed from twitter followers[C]∥36th International ACM SIGIR Conference on Research and Development in Information Retrieval.Dublin,Ireland,2013:283-292.
[7]WANG L C,MENG X W,ZHANG Y J.Context-Aware recommender systems:A survey of the state-of-the-art and possible extensions[J].Journal of Software,2012,23(1):1-20.
[8]SYMEONIDIS P.ClustHOSVD:Item Recommendation by Com- bining Semantically Enhanced Tag Clustering With Tensor HOSVD[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2016,46(9):1240-1251.
[9]MISTRY O,SEN S.Tag recommendation for social bookmar- king:Probabilistic approaches [J].Multiagent and Grid Systems,2012,8(2):143-163.
[10]于洪,李俊华.一种解决新项目冷启动问题的推荐算法[J].软件学报,2015,26(6):1395-1408.
[11]ZHANG Z K,LIU C,ZHANG Y C,et al.Solving the cold-start problem in recommender systems with social tags [J].EPL (Europhysics Letters),2010,92(2):28002.
[12]ZHANG Z K,ZHOU T,ZHANG Y C.Tag-Aware recommender systems:A state-of-the-art survey [J].Journal of Computer Science and Technology,2011,26(5):767-777.
[13]ZHANG Z K,ZHOU T,ZHANG Y C.Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs[J].Physica A:Statistical Mechanics and its Applications,2010,389(1):179-186.
[14]刘建勋,石敏,周栋,等.基于主题模型的Mashup标签推荐方法[J].计算机学报,2017,40(2):520-534.
[15]李锡荣,许洁萍,薛盛博,等.基于软近邻投票的图像标签相关性计算[J].计算机学报,2014,37(6):1365-1371.
[16]张斌,张引,高克宁,等.融合关系与内容分析的社会标签推荐[J].软件学报,2012,23(3):476-488.
[17]YANG S,LU Z,GILES C L.Automatic tag recommendation algorithms for social recommender systems [J].ACM Transactions on the Web,2011,5(1):1-31.
[18]孔欣欣,苏本昌,王宏志,等.基于标签权重评分的推荐模型及算法研究[J].计算机学报,2017,40(6):1440-1452.
[19]JOMSRI P,SANGUANSINTUKUL S,CHOOCHAIWATTANA W.A framework for tag-based research paper recommender system:An IR approach[C]∥Proceedings of the 2010 IEEE 24th Int’l Conf.on Advanced Information Networking and Applications Workshops.2010:103-108.
[20]郭彩云,王会进.改进的基于标签的协同过滤算法[J].计算机工程与应用,2016,52(8):56-61,147.
[21]李慧,马小平,胡云,等.融合主题与语言模型的个性化标签推荐方法研究[J].计算机科学,2015,42(8):70-74.
[22]AR Y,BOSTANCI E.A genetic algorithm solution tothe colla- borative filtering problem[J].Expert Systemswith Applications,2016,61:122-128.
[23]李瑞敏,林鸿飞,闫俊.基于用户-标签-项目语义挖掘的个性化音乐推荐[J].计算机研究与发展,2014,51(10):2270-2276.
[24]BREESE J S,HECKERMAN D,KADIE C.Empirical analysis of predictive algorithms for collaborative filtering[C]∥Proc.of the 14th Conference on Uncertainty in Artificial Intelligence.Madison,Wisconsin,USA,1998:43-52.
[25]HERLOCKER J L,KONSTAN J A,BORCHERS A,et al.An algorithmic framework for performing collaborative filtering[C]∥22th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.Berkeley,CA,USA,1999:230-237.
[26]JIN R,CHAI J Y,SI L.An automatic weighting scheme for collaborative filtering[C]∥27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.Sheffield,UK,2004:337-344.
[27]RESNICK P,IACOVOU N,SUCHAK M,et al.An open architecture for collaborative filtering of netnews[C]∥1994 ACM Conference on Computer Supported Cooperative Work.Chapel Hill,NC,USA,1994:175-186.
[28]SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]∥10th International Conference on World Wide Web.Hong Kong,China,2001:285-295.
[29]DESHPANDE M,KARYPIS G.Item-based top-n recommendation algorithms[J].ACM Transactions on Information System,2004,22(1):143-177.
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