A Survey of Recommendation Systems in Big Data

doi:10.13190/j.jbupt.2015.02.001

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2015, Vol. 38 ›› Issue (2): 1-15.doi: 10.13190/j.jbupt.2015.02.001

• Review • Next Articles

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:2014-12-29 Online:2015-04-28 Published:2015-05-14

Abstract

Abstract:

Information overload is one of most critical problems in big data, and recommendation systems which are powerful methods to solve this problem are coming under growing attention by industry and academia. The main task of recommendation systems in big data is to improve the performance and accuracy along with user satisfaction utilizing user feedback, social network and other information. A survey of the recommendation systems in the big data is proposed, which includes the summarization of big data and recommendation systems, the differences between the recommendation systems in traditional environment and in big data, key techniques, evaluation and typical applications according to a hierarchical framework. Finally, the prospects for future development and suggestions for possible extensions are also discussed.

Key words: big data, recommendation systems, key technology

CLC Number:

TP18

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.

References

[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]//8^th 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 6^th 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 8^th 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]//16^th 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]//17^th 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 8^th 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 8^th 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 8^th 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 7^th 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 6^th 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]//13^th 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 3^rd 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]//20^th 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]//5^th 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]//8^th 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]//6^th 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]//7^th 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]//17^th 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 8^th 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]//8^th 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]//20^th 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]//7^th 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]//18^th 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]//10^thInternational 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]//7^th 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]//6^thACM 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]//8^th 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]//3^rd 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]//8^th 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]//17^thACM 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]//28^th 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]//1^st 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]//23^rd 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]//1^stInternational 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]//20^th 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]//16^th 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]//8^th 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]//5^th 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]//8^th 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]//8^th 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.

A Survey of Recommendation Systems in Big Data

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 8

Recommended Articles

Metrics

Comments

[1]	CHANG Xiao, HUANG Zhibin, YU Min, YANG Wubing. A Deep Decision Tree Model for Aerospace Big Data [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(3): 1-6.
[2]	YAO Jun-liang, XUE Hai-tao, LIU Qing. Daily Water Volume Prediction Algorithm of Urban Smart Water Based on Big Data [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(4): 82-88.
[3]	WANG Ya-qing, GUO Cai-li, CHU Yun-fei, ZHOU Hong-hong, FENG Chun-yan. Personalized Hierarchical Recurrent Model for Session-Based Recommendation Systems [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(6): 142-148.
[4]	YI Zhi-ling, CUI Chun-feng, HAN Shuang-feng, PAN Cheng-kang, CHEN Ya-mi. Analysis of Key Technologies of 5G-Oriented Cellular Internet of Things [J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(5): 20-25.
[5]	ZHANG Si-hai, ZHANG Jian-hua, CHEN Ying, ZHU Jin-kang. Wireless Big Data Enabled Emerging Technologies for Beyond 5G System [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(5): 52-61.
[6]	LI Fang-wei, NIE Yi-fang, LIU Fan, ZHU Jiang. The Mechanism of Situation Data Fusion Based on Trust in Wireless Sensor Network [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2015, 38(5): 71-76.
[7]	GAO Zhi-peng, NIU Kun, LIU Jie. Analytics Towards Big Data [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2015, 38(3): 1-12.
[8]	. A Survey of Ubiquitous Network [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2010, 33(5): 1-6.