期刊文献+

社会化推荐系统研究 被引量:140

Research on Social Recommender Systems
下载PDF
导出
摘要 近年来,社会化推荐系统已成为推荐系统研究领域较为活跃的研究方向之一.如何利用用户社会属性信息缓解推荐系统中数据稀疏性和冷启动问题、提高推荐系统的性能,成为社会化推荐系统的主要任务.对最近几年社会化推荐系统的研究进展进行综述,对信任推理算法、推荐关键技术及其应用进展进行前沿概括、比较和分析.最后,对社会化推荐系统中有待深入研究的难点、热点及发展趋势进行展望. Social recommender systems have recently become one of the hottest topics in the domain of recommender systems. The main task of social recommender system is to alleviate data sparsity and cold-start problems, and improve its performance utilizing users' social attributes. This paper presents an overview of the field of social recommender systems, including trust inference algorithms, key techniques and typical applications. The prospects for future development and suggestions for possible extensions are also discussed.
出处 《软件学报》 EI CSCD 北大核心 2015年第6期1356-1372,共17页 Journal of Software
基金 国家自然科学基金(60872051) 北京市教育委员会共建项目
关键词 推荐系统 协同过滤 信任推理 矩阵分解 因子分解机 recommender system collaborative filtering trust inference matrix factorization factorization machine
  • 相关文献

参考文献93

  • 1Wang Z, Sun LF, Zhu WW, Yang SQ, Li HZ, Wu DP. Joint social and content recommendation for user-generated videos in online social network. IEEE Trans. on Multimedia, 2013,15(3):698-710. [doi: 10.1109/TMM.212.2237022].
  • 2Quijano-Sanchez L, Recio-Garcia J, Diaz-Agudo B. Social factors in group recommender systems. ACM Trans. on Intelligent Systems and Technology, 2013,4(1):Article No.8. [doi: 10.1145/2414425/2414433].
  • 3Social Network Analysis. A brief introduction. 2007. http://orgnet.Com/sna.html.
  • 4Jamali M, Ester M. A transitivity aware matrix factorization model for recommendation in social networks. In: Proc. of the IJCAI. AAAI Press, 2011. 2644-2649. [doi: IO.5591/978-1-57735-516-8/IJCAI11-440].
  • 5Jamali M, Ester M. A matrix factorization technique with trust propagation tbr recommendation in social networks. In: t'roe, ot tlae ReeSys 2010. New York: ACM Press, 2010. 135-142. [doi: 10.1145/1864708.1864736].
  • 6Moghaddam S, Jamali M, Ester M. ETF: Extended tensor factorization model for personalizing prediction of review helpfulness. In: Proc. of the ACM WSDM 2012. New York: ACM Press, 2012. 163-172. Idol: 10.1145/2124295.2124316].
  • 7Hoens TR, Blanton M. A private and reliable recommendation system for social networks. In: Proc. of the IEEE SocialCom. Washington: IEEE Computer Society, 2011. 816-825. Idol: 10.1109/SocialCom.2010.124].
  • 8Berjani B, Strufe T. A recommendation system for spots in location-based online social networks. In: Proc. of the SNS 2011. New York: ACM Press, 201 I. 1-4. [doi: 10.I 145/1989656.1989660].
  • 9Kim HK, Ryu YU. Cho Y. Customer-Driven content recommendation over a network of customers. IEEE Trans. on Systems, Man and Cybernetics, Part A, 2012,42(1):48-56.
  • 10Purushotham S, Liu Y, Kuo CC. Collaborative topic regression with matrix factorization for recommendation systems. In: Proc. of the ICML. 2012. 759-766.

二级参考文献11

  • 1Jonna H, Albrecht S, Jani M, et al.. Context-aware mobile media and social networks. Proceedings of the llth International Conference on Human-Computer Interaction with Mobile Devices and Services, Bonn, Germany, 2009: 1-3.
  • 2Ricci F. Mobile recommender systems. International Journal of Information Technology and Tourism, 2011, 12(3): 205-231.
  • 3Wang L C, Meng X W, Zbang Y J, et al.. New approaches to mood-based hybrid collaborative filtering. Proceedings of the Workshop on Context-Aware Movie Recommendation at the 4th ACM Conference on Recommender Systems (ACM Recsys'10), Barcelona, Spain, 2010: 28-33.
  • 4Gediminas A and Alexander T. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 152-162.
  • 5Matthias B and Gernot B. Improving the recommendation of mobile services by interpreting the user's icon arrangement. Proceedings of the llth International Conference on Human-Computer Interaction with Mobile Devices and Services, Bonn, Germany, 2009: 15-18.
  • 6Gupta A, Kalra A, Boston D, et al.. MobiSoC: a middleware for mobile social computing applications. Mobile Networks and Applications, 2009, 14(10): 35-52.
  • 7Arazy O, Kumar N, and Shapira B. Improving social recommender systems. IT Professional, 2009, 11(4): 38-44.
  • 8Dijiang H and Vetri A. Email-based social network trust. IEEE International Conference on Social Computing /IEEE International Conference on Privacy, Security, Risk and Trust Boston. USA , 2010: 363-370.
  • 9Herlocker J, Konstan J, Terveen L, et al.. Evaluating collaborative filtering recommender systems. A CM Transactions on Information Systems, 2004, 22(1): 20-21.
  • 10Fernando D and Pedro G C. Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset. Proceedings of the Workshop on Context-Aware Movie Recommendation at the 4th ACM Conference on Recommender Systems (ACM Recsys'10), Barcelona, Spain, 2010: 45-52.

共引文献25

同被引文献772

引证文献140

二级引证文献826

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部