北京邮电大学学报 ›› 2015, Vol. 38 ›› Issue (2): 1-15.doi: 10.13190/j.jbupt.2015.02.001
• 综述 • 下一篇
大数据环境下的推荐系统
孟祥武, 纪威宇, 张玉洁
- 北京邮电大学 智能通信软件与多媒体北京市重点实验室, 北京 100876
-
收稿日期:
2014-12-29出版日期:
2015-04-28发布日期:
2015-05-14 -
作者简介:
孟祥武(1966—), 男, 教授, 博士生导师, E-mail: mengxw@bupt.edu.cn. -
基金资助:
国家自然科学基金项目(60872051); 北京市教育委员会共建项目
A Survey of Recommendation Systems in Big Data
MENG Xiang-wu, JI Wei-yu, ZHANG Yu-jie
- Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China
-
Received:
2014-12-29Online:
2015-04-28Published:
2015-05-14
摘要:
信息过载是大数据环境下最严重的问题之一,推荐系统作为有效缓解该问题的方法,受到工业界和学术界越来越多的关注. 如何充分利用丰富的用户反馈、社会化网络等信息进一步提高推荐系统的性能和用户满意度,成为大数据环境下推荐系统的主要任务. 首先,对近几年大数据环境下的推荐系统进行了综述,对大数据和推荐系统进行了概述,对推荐系统在传统环境下和大数据环境下的区别进行了辨析;然后,根据层次化的框架对推荐系统关键技术、效用评价以及应用实践等进行了概括、比较和分析;最后,对大数据环境下推荐系统有待深入研究的难点和发展趋势进行了展望.
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引用本文
孟祥武, 纪威宇, 张玉洁. 大数据环境下的推荐系统[J]. 北京邮电大学学报, 2015, 38(2): 1-15.
MENG Xiang-wu, JI Wei-yu, ZHANG Yu-jie. A Survey of Recommendation Systems in Big Data[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2015, 38(2): 1-15.
[1] 李国杰, 程学旗. 大数据研究: 未来科技及经济社会发展的重大战略领域—大数据的研究现状与科学思考[J]. 中国科学院院刊, 2012, 27(6): 647-657. Li Guojie, Cheng Xueqi. Research status and scientific thinking of big data[J]. Bulletin of Chinese Academy of Sciences, 2012, 27(6): 647-657.[2] Labrinidis A, Jagadish H V. Challenges and opportunities with big data[J]. Proceedings of the VLDB Endowment, 2012, 5(12): 2032-2033.[3] 孟祥武, 胡勋, 王立才, 等. 移动推荐系统及其应用[J]. 软件学报, 2013, 24(1): 91-108. Meng Xiangwu, Hu Xun, Wang Licai, et al. Mobile recommender systems and their applications[J]. Journal of Software, 2013, 24(1): 91-108.[4] Ye Tao, Bickson D, Yan Qiang. Second workshop on large-scale recommender systems: research and best practice[C]//8th ACM Conference on Recommender Systems, 2014 ACM. Silicon Valley: ACM Press, 2014: 385-386.[5] 孟祥武, 王凡, 史艳翠, 等. 移动用户需求获取技术及其应用[J]. 软件学报, 2014, 25 (3): 439- 456. Meng Xiangwu, Wang Fan, Shi Yancui, et al. Mobile user requirements acquisition techniques and their applications[J]. Journal of Software, 2014, 25 (3): 439-456.[6] 王鑫, 黄忠义. 网络资源中基于K-Means 聚类的个性化推荐[J]. 北京邮电大学学报, 2014, 37(S1): 120-124. Wang Xin, Huang Zhongyi. Network resource personalized recommendation based on K-Means clustering[J]. Journal of Beijing University of Posts and Telecommunications, 2014, 37(S1): 120-124.[7] Hong Jongyi, Suh E H, Kim J, et al. Contextaware system for proactive personalized service based on context history[J]. Expert Systems with Applications, 2009, 36(4): 7448-7457.[8] Pessemier T D, Deryckere T, Martens L. Extending the Bayesian classifier to a context-aware recommender system for mobile devices[C]//Internet and Web Applications and Services (ICIW), 2010 Fifth International Conference on IEEE. Barcelona, Spain: IEEE Press, 2010: 242-247.[9] Shahabi C, Chen Yishin. An adaptive recommendation system without explicit acquisition of user relevance feedback[J]. Distributed and Parallel Databases, 2003, 14(2): 173-192.[10] 鄂海红, 宋美娜, 李川, 等. 结合时间上下文挖掘学习兴趣的协同过滤推荐算法[J]. 北京邮电大学学报, 2014, 37(6): 49-53. E Haihong, Song Meina, Li Chuan, et al. A collaborative filtering recommendation algorithm with time context for learning interest mining[J]. Journal of Beijing University of Posts and Telecommunications, 2014, 37(6): 49-53.[11] 王立才, 孟祥武, 张玉洁. 上下文感知推荐系统[J]. 软件学报, 2012, 23(1): 1-20. Wang Licai, Meng Xiangwu, Zhang Yujie. Context-aware recommender systems[J]. Journal of Software, 2012, 23(1): 1-20.[12] 程学旗, 靳小龙, 王元卓, 等. 大数据系统和分析技术综述[J]. 软件学报, 2014, 25(9): 1889-1908. Cheng Xueqi, Jin Xiaolong, Wang Yuanzhuo, et al. Survey on big data system and analytic technology[J]. Journal of Software, 2014, 25(9): 1889-1908.[13] 何清, 李宁, 罗文娟, 等. 大数据环境下的机器学习算法综述[J]. 模式识别与人工智能, 27(4): 327-336. He Qing, Li Ning, Luo Wenjuan, et al. A survey of machine learning algorithms for big data[J]. Patten Recognition and Aitificial Intelligence, 2014, 27(4): 327-336.[14] 印鉴, 王智圣, 李琪, 等. 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. Yin Jian, Wang Zhisheng, Li Qi, et al. Personalized recommendation based on largescale implicit feedback[J]. Journal of Software, 2014, 25 (9): 1953-1966.[15] Yang Diyi, Chen Tianqi, Zhang Weinan, et al. Local implicit feedback mining for music recommendation[C]//the 6th ACM Conference on Recommender Systems, 2012 ACM. Dublin: ACM Press, 2012: 91-98.[16] Rafailidis D, Nanopoulos A. Modeling the dynamics of user preferences in coupled tensor factorization[C]//the 8th ACM Conference on Recommender Systems, 2014 ACM. Silicon Valley: ACM Press, 2014: 321-324.[17] Oh K J, Lee W J, Lim C G, et al. Personalized news recommendation using classified keywords to capture user preference[C]//16th Advanced Communication Technology (ICACT), 2014 International Conference on IEEE. South Korea: IEEE Press, 2014: 1283-1287.[18] Takács G, Pilászy I, Németh B, et al. Scalable collaborative filtering approaches for large recommender systems[J]. The Journal of Machine Learning Research, 2009, 10(12): 623-656.[19] Bhagat S, Weinsberg U, Loannidis S, et al. Recommending with an agenda: active learning of private attributes using matrix factorization[J]. ArXiv Preprint ArXiv, 2013: 1311-1321.[20] 丁伟峰, 郑小林, 陈德人. 基于PureSVD模型的协同过滤主动采样[J]. 北京邮电大学学报, 2013, 36(4): 23-26. Ding Weifeng, Zheng Xiaolin, Chen Deren. Active sampling based on Pure SVD model for collaborative filtering[J]. Journal of Beijing University of Posts and Telecommunications, 2013, 36(4): 23-26.[21] Dror G, Koenigstein N, Koren Y, et al. The Yahoo! Music Dataset and KDD-Cup'11[C]//17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2011 ACM SIGKDD. San Diego, CA: ACM Press, 2012: 8-18.[22] 涂丹丹, 舒承椿, 余海燕. 基于联合概率矩阵分解的上下文广告推荐算法[J]. 软件学报, 2013, 24 (3): 454-464. Tu Dandan, Shu Chengchun, Yu Haiyan. Using unified probabilistic matrix factorization for contextual advertisement recommendation[J]. Journal of Software, 2013, 24(3): 454-464.[23] Bauer J, Nanopoulos A. A framework for matrix factorization based on general distributions[C]//Proceedings of the 8th ACM Conference on Recommender Systems. Silicon Valley: ACM Press, 2014: 249-256.[24] Cheng Chen, Xia Fen, Zhang Tong, et al. Gradient boosting factorization machines[C]//Proceedings of the 8th ACM Conference on Recommender Systems. Silicon Valley: ACM Press, 2014: 265-272.[25] Pálovics R, Benczúr A A, Kocsis L, et al. Exploiting temporal influence in online recommendation[C]//Proceedings of the 8th ACM Conference on Recommender Systems. Silicon Valley: ACM Press, 2014: 273-280.[26] Schelter S, Boden C, Schenck M, et al. Distributed matrix factorization with mapreduce using a series of broadcast-joins[C]//Proceedings of the 7th ACM Conference on Recommender Systems. Hongkong: ACM Press, 2013: 281-284.[27] Diaz-Aviles E, Drumond L, Schmidt-Thieme L, et al. Real-time top-n recommendation in social streams[C]//Proceedings of the 6th ACM Conference on Recommender Systems. Dublin: ACM Press, 2012: 59-66.[28] Ge Yong, Xiong Hui, Tuzhilin A, et al. Costaware collaborative filtering for travel tour recommendations[J]. ACM Transactions on Information Systems (TOIS), 2014, 32(1): 4-28.[29] Golub G, Kahan K. Calculating the singular values and pseudo-inverse of a matrix[J]. Journal of the Society for Industrixal and Applied Mathematics, 1965, 2(2): 205-224.[30] Lee D D, Seung Hs. Algorithms for non-negative matrix factorization[C]//13th Advances in Neural Information Processing Systems, NIPS 2000. Denver, USA: MIT Press, 2000: 556-562.[31] Zheng V W, Zheng Yu, Xie Xing, et al. Towards mobile intelligence: learning from GPS history data for collaborative recommendation[J]. Artificial Intelligence, 2012, 184(6): 17-37.[32] Symeonidis P, Papadimitriou A, Manolopoulos Y, et al. Geo-social recommendations based on incremental tensor reduction and local path traversal[C]//Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks. Chicago, USA: ACM Press, 2011: 89-96.[33] Koren Y. Collaborative filtering with temporal dynamics[J]. Communications of the ACM, 2010, 53(4): 89-97.[34] Salakhutdinov R, Mnih A. Probabilistic matrix factorization[C]//20th Advances in Neural Information Processing Systems, NIPS 2007. Vancouver, Canade: MIT Press, 2007, 20(3): 432-451.[35] 朱郁筱, 吕琳媛. 推荐系统评价指标综述[J]. 电子科技大学学报, 2012, 41(2): 163 -175. Zhu Yuxiao, Lü Linyuan. Evaluation metrics for recommender systems[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(2): 163-175.[36] Isaacman S, Ioannidis S, Chaintreau A, et al. Distributed rating prediction in user generated content streams[C]//5th ACM Conference on Recommender Systems, 2011 ACM. Chicago: ACM Press, 2011: 69-76.[37] Bachrach Y, Finkelstein Y, Gilad-Bachrach R, et al. Speeding up the Xbox recommender system using a Euclidean transformation for inner- product spaces[C]//8th ACM Conference on Recommender Systems, 2014 ACM. Silicon Valley: ACM Press, 2014: 250-257.[38] Takács G, Tikk D. Alternating least squares for personalized ranking[C]//6th ACM Conference on Recommender Systems, 2012 ACM. Dublin: ACM Press, 2012: 83-90.[39] Zhuang Yong, Chin Weisheng, Juan Yuchin, et al. A fast parallel SGD for matrix factorization in shared memory systems[C]//7th ACM Conference on Recommender Systems, 2013 ACM. Hong Kong: ACM Press, 2013: 249-256.[40] Recht B, Ré C. Parallel stochastic gradient algorithms for large-scale matrix completion[J]. Mathematical Programming Computation, 2013, 5(2): 201-226.[41] Gemulla R, Nijkamp E, Haas P J, et al. Largescale matrix factorization with distributed stochastic gradient descent[C]//17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011 ACM. San Diego: ACM Press, 2011: 69-77.[42] Petroni F, Querzoni L. GASGD: stochastic gradient descent for distributed asynchronous matrix completion via graph partitioning[C]//the 8th ACM Conference on Recommender Systems. Silicon Valley: ACM, 2014: 241-248.[43] Fazeli S, Loni B, Bellogin A, et al. Implicit vs explicit trust in social matrix factorization[C]//8th ACM Conference on Recommender Systems, 2014 ACM. Silicon Valley: ACM Press, 2014: 317-320.[44] McGinty L, Reilly J. On the evolution of critiquing recommenders[M]. US: Springer, 2011: 419-453.[45] Zhao Xin Wayne, Guo Yanwei, He Yulan, et al. We know what you want to buy: a demographic-based system for product recomme ndation on microblogs[C]//20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014 ACM. New York: ACM Press, 2014: 1935-1944.[46] Töscher A, Jahrer M. Collaborative filtering ens emble for ranking[J]. Journal of Machine Learning Research W&CP, 2012, 18: 61-74.[47] Guo Guibing. Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems[C]//7th ACM Conference on Recommender Systems, 2013 ACM. Hong Kong: ACM Press, 2013: 451-454.[48] Zheng Yu, Xie Xing, Ma Weiying. GeoLife : a collaborative social networking service among user, location and trajectory[J]. IEEE Data Eng. Bull, 2010, 33(2): 32-39.[49] Zheng Yu, Zhang Lizhu, Xie Xing, et al. Mining interesting locations and travel sequences from GPS trajectories[C]//18th International Conference on World Wide Web , 2009 ACM. Spain: ACM Press, 2009: 791-800.[50] Zheng Yu, Li Quannan, Chen Yukun, et al. Understanding mobility based on GPS data[C]//10th International Conference on Ubiquitous Computing. Seoul, 2008 ACM. South Korea: ACM Press, 2008: 312-321.[51] Hu Bo, Ester M. Spatial topic modeling in online social media for location recommendation[C]//7th ACM Conference on Recommender Systems, 2013 ACM. Hong Kong: ACM Press, 2013: 25-32.[52] Yang Xiwang, Steck H, Guo Yang, et al. On top-k recommendation using social networks[C]//6th ACM Conference on Recommender Systems, 2012 ACM. Dublin: ACM Press, 2012: 67-74.[53] Zhang Kunpeng, Ouksel A, Fan Shaokun, et al. Scalable audience targeted models for brand advertising on social networks[C]//8th ACM Conference on Recommender Systems, 2014 ACM. Silicon Valley: ACM Press, 2014: 341- 344.[54] Wang Jing, Zhao Hui. Social group recommendation using topic models[J]. Journal of Chemical and Pharmaceutical Research, 2014, 6(7): 679-684.[55] Purushotham S, Kuo C C J, Shahabdeen J, et al. Collaborative group-activity recommendation in location-based social networks[C]//3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, 2014 ACM. Dallas: ACM Press, 2014: 8-15.[56] Naamani-Dery L, Kalech M, Rokach L, et al. Preference elicitation for narrowing the recommended list for groups[C]//8th ACM Conference on Recommender Systems, 2014 ACM. Silicon Valley: ACM Press, 2014: 333- 336.[57] Ferreira Cordeiro R L, Traina Junior C, Machado Traina A J, et al. Clustering very large multi-dimensional datasets with mapreduce[C]//17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011 ACM. San Diego: ACM Press, 2011: 690-698.[58] Yu Yuantse, Huang Chungming, Lee Yuntz. An intelligent touring system based on mobile social network and cloud computing for travel recommendation[C]//28th International Conference on Advanced Information Networking and Applications Workshops(AINA), 2014 IEEE. Victoria, Canada: IEEE Press, 2014: 19-24.[59] Katkar V D, Kulkarni S V. A novel parallel implementation of naive Bayesian classifier for big data[C]//2013 International Conference on Green Computing, Communication and Conservation of Energy(ICGCE), 2013 IEEE. India: IEEE Press, 2013: 847-852.[60] Walunj S G, Sadafale K. An online recomm endation system for e-commerce based on apache mahout framework[C]//2013 Annual Conference on Computers and People Research, 2013 ACM. Cincinnati: ACM Press, 2013: 153-158.[61] Xu Shengwu, Xia Zhengyou. Hot news recommendation system across heterogonous websites using Hadoop[C]//Advanced Materials Research, 2014 TTP. Switzerland: TTP Press, 2014, 989: 4704-4707.[62] Gong Songjie, Xu Jiongbo. Electronic commerce personalized recommendation model under cloud computing environment[J]. Applied Mechanics and Materials, 2014, 513(2): 639- 642.[63] Franco-Arcega A, Carrasco-Ochoa J A , Sanchez-Diaz G, et al. Building fast decision trees from large training sets[J]. Intelligent Data Analysis, 2012, 16 (4): 649-664.[64] Xue Zhenghua, Shen Geng, Li Jianhui, et al. ComPression-aware I/O performance analysis for big data clustering[C]//1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, 2012 ACM. Beijing, China: ACM Press, 2012: 45-52.[65] Kalyanakrishnan S, Singh D, Kant R. On building decision trees from large-scale data in applications of on-line advertising[C]//23rd ACM International Conference on Information and Knowledge Management (CIKM), 2014 ACM. Shanghai: ACM Press, 2014: 669-678.[66] Yang Hang, Fong Simon. Incrementally optimized decision tree for noisy big data[C]//1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications , 2012 ACM. Beijing: ACM Press, 2012: 36-44.[67] Lu E H C, Chen Chinyu, Tseng V S. Personalized trip recommendation with multiple constraints by mining user check-in behaviors[C]//20th International Conference on Advances in Geographic Information Systems, 2012 ACM. California: ACM Press, 2012: 209-218.[68] Lee W J, Oh K J, Lim C G, et al. User profile extraction from Twitter for personalized news recommendation[C]//16th International Conference on Advanced Communication Technology, 2014 IEEE. Korea: IEEE Press, 2014: 779-783.[69] Yi Xing, Hong Liangjie, Zhong Erheng, et al. Beyond clicks: dwell time for personalization[C]//8th ACM Conference on Recommender Systems, 2014 ACM. Silicon Valley: ACM Press, 2014: 113- 120.[70] Vargas S, Castells P. Rank and relevance in novelty and diversity metrics for recommender systems[C]//5th ACM Conference on Recommender Systems, 2011 ACM. Chicago: ACM Press, 2011: 109-116.[71] Mayer-Schönberger V, Cukier K. Big data: arevolution that will transform how we live, work, and think[M]. US: Houghton Mifflin Harcourt, 2013: 1-261.[72] Vargas S, Castells P. Improving sales diversity by recommending users to items[C]//8th ACM Conference on Recommender Systems, 2014 ACM. Silicon Valley: ACM Press, 2014: 145- 152.[73] Garcin F, Faltings B, Donatsch O, et al . Offline and online evaluation of news recommender systems at swissinfo. ch[C]//8th ACM Conference on Recommender Systems, 2014 ACM. Silicon Valley: ACM Press, 2014: 169-176.[74] Becchetti L, Bergamini L, Colesanti U M , et al. A lightweight privacy preserving SMS-based recommendation system for mobile users[J]. Knowledge and Information Systems, 2014, 40(1): 49-77.[75] 霍峥, 孟小峰. 轨迹隐私保护技术研究[J]. 计算机学报, 2011, 34(10): 1820-1830. Huo Zheng, Meng Xiaofeng. A survey of trajectory privacy-preserving techniques[J]. Chinese Journal of Computer, 2011, 34(10): 1820-1830. |
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