Journal Home Online First Current Issue Archive For Authors Journal Information 中文版

Engineering >> 2016, Volume 2, Issue 2 doi: 10.1016/J.ENG.2016.02.013

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

Advanced Analytics Institute, University of Technology Sydney, Sydney, NSW 2007, Australia

Received: 2015-12-23 Revised: 2016-05-04 Accepted: 2016-06-12 Available online: 2016-06-30

Next Previous

Abstract

While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services. A critical reason for such bad recommendations lies in the intrinsic assumption that recommended users and items are independent and identically distributed (IID) in existing theories and systems. Another phenomenon is that, while tremendous efforts have been made to model specific aspects of users or items, the overall user and item characteristics and their non-IIDness have been overlooked. In this paper, the non-IID nature and characteristics of recommendation are discussed, followed by the non-IID theoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendation problems, from the perspective of both couplings and heterogeneity. This non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research and can hopefully deliver informed, relevant, personalized, and actionable recommendations. It creates exciting new directions and fundamental solutions to address various complexities including cold-start, sparse data-based, cross-domain, group-based, and shilling attack-related issues.

Figures

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

References

[ 1 ] Jannach D, Zanker M, Felfernig A, Friedrich G. Recommender systems: an introduction. Cambridge: Cambridge University Press; 2010.

[ 2 ] Ricci F, Rokach L, Shapira B, Kantor PB, editors. Recommender systems handbook. 2nd ed. New York: Springer; 2015.

[ 3 ] Cao L. Data science: a comprehensive overview. Technical report. Sydney: University of Technology Sydney; 2016.

[ 4 ] McKinsey Global Institute; Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Big data: the next frontier for innovation, competition, and productivity. New York: McKinsey Global Institute; 2011.

[ 5 ] Cao L. Non-IIDness learning in behavioral and social data. Comput J 2014;57(9):1358–70. link1

[ 6 ] Cao L. Coupling learning of complex interactions. Inform Process Manag 2015;51(2):167–86. link1

[ 7 ] Cao L. In-depth behavior understanding and use: the behavior informatics approach. Inform Sciences 2010;180(17):3067–85. link1

[ 8 ] Cao L. Yu PS, editors. Behavior computing: modeling, analysis, mining and decision. London: Springer; 2012.

[ 9 ] Fu B, Xu G, Cao L, Wang Z, Wu Z. Coupling multiple views of relations for recommendation. In: Cao T, Lim EP, Zhou ZH, Ho TB, Cheung D, Motoda H, editors Advances in Knowledge Discovery and Data mining: 19th Pacific-Asia Conference, Part II; 2015 May 19-22; Ho Chi Minh City, Vietnam. Switzerland: Springer International Publishing; 2015. p. 723–43.

[10] Li T, Lu J,?López LM. Preface: intelligent techniques for data science. Int J Intell Syst 2015;30(8):851–3. link1

[11] Yu Y, Wang C, Gao Y, Cao L, Chen X. A coupled clustering approach for items recommendation. In: Pei J, Tseng VS, Cao L, Motoda H, Xu G, editors Advances in Knowledge Discovery and Data mining: 17th Pacific-Asia Conference, Part II; 2013 Apr 14-17; Gold Coast, Australia. Heidelberg: Springer; 2013. p. 365–76.

[12] Cao L, Yu PS. Non-IID recommendation theories and systems. IEEE Intell Syst 2016;31(2):81–4.

[13] Cao L. Data science and analytics: a new era. Int J Data Sci Analyt 2016;1(1):1–2. link1

[14] Cao L. Data science: intrinsic challenges and directions. Technical report. Sydney: University of Technology Sydney; 2016.

[15] Cao L. Data science: nature and pitfalls. Technical report. Sydney: University of Technology Sydney; 2016.

[16] Su X, Khoshgoftaar TM. A survey of collaborative filtering techniques. Adv Artif Intell 2009;2009(4):1–19.

[17] Koren Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2008 Aug 24-27; New York, USA;?2008. p. 426–34.

[18] Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on the World Wide Web; 2001 May 1-5; Hong Kong, China; 2001. p. 285–95.

[19] Deshpande M, Karypis G. Item-based top-N recommendation algorithms. ACM Trans Inform Syst 2004;22(1):143–77. link1

[20] Ma H, Yang H, Lyu MR, King I. SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management; 2008 Oct 26-30; Napa Valley, CA, USA; 2008. p. 931–40.

[21] Ma H. An experimental study on implicit social recommendation. In: Proceedings of the 36th International ACM SIGIR conference on Research and Development in Information Retrieval; 2013 Jul 28-Aug 1; Dublin, Ireland; 2013. p. 73–82.

[22] Singh AP, Gordon GJ. Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2008 Aug 24-27; Las Vegas, NV, USA; 2008. p. 650–8.

[23] Hu L, Cao J, Xu G, Cao L, Gu Z, Cao W. Deep modeling of group preferences for group-based recommendation. In: Proceedings?of?the 28th AAAI Conference on Artificial Intelligence; 2014 Jul 27-31;?Québec City, Canada; 2014. p. 1861–7.

[24] Hu L, Cao J, Xu G, Wang J, Gu Z, Cao L. Cross-domain collaborative filtering via bilinear multilevel analysis. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence; 2013 Aug 3-9; Beijing, China; 2013. p. 1–7.

[25] Hu L, Cao J, Xu G, Cao L, Gu Z, Zhu C. Personalized recommendation via cross-domain triadic factorization. In:?Proceedings of the 22nd International Conference on World Wide Web; 2013 May 13-17; Rio de Janeiro, Brazil; 2013. p. 595–606.

[26] Yang X, Steck H, Liu Y. Circle-based recommendation in online social networks. In: Proceedings of the 18th ACM SIGKDD Knowledge Discovery and Data Mining; 2012 Aug 12-16; Beijing, China; 2012. p. 1267–75.

[27] Li F, Xu G, Cao L, Fan X, Niu Z. CGMF: coupled group-based matrix factorization for recommender system. In: Lin X, Manolopoulos Y, Srivastava D, Huang G, editors Web Information Systems Engineering—WISE 2013: 14th International Conference, Part I; 2013 Oct 13-15; Nanjing, China. Heidelberg: Springer. 2013. p. 189–98.

[28] Breese JS, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In:?Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence; 1998 Jul 24-26; Madison, WI, USA. San Francisco: Morgan Kaufmann Publishers Inc.; 1998. p. 43–52.

[29] Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work; 1994 Oct 22-26; Chapel Hill, NC, USA; 1994. p. 175–86.

[30] Alter O, Brown PO, Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci USA 2000;97(18):10101–6. link1

[31] Salakhutdinov R, Mnih A. Probabilistic matrix factorization. In:?Platt?JC,?Koller D,?Singer?Y,?Roweis ST, editors Proceedings of ?the 21st Annual Conference on Neural Information Processing Systems 2007; 2007 Dec 3-6; Vancouver, Canada; 2007. p. 1257–64.

[32] Burke R. Hybrid web recommender systems. In:?Brusilovsky P, Kobsa A, Nejdl W, editors The adaptive web. Heidelberg: Springer; 2007. p. 377–408.

[33] Burke R. Hybrid recommender systems: survey and experiments. User Model User-Adapt Interac 2002;12(4):331–70. link1

[34] Lv LL,?Medo M,?Yeung CH,?Zhang YC,?Zhang ZK,?Zhou T. Recommender systems. Phys Rep 2012;519(1):1–49. link1

[35] Konstan JA, Riedl J. Recommender systems: from algorithms to user experience. User Model User-Adapt Interact 2012;22(1):101–23. link1

[36] Bobadilla J, Ortega F, Hernando A, Gutiérrez A. Recommender systems survey. Knowl-Based Syst 2013;46:109–32. link1

[37] Park DH, Kim HK, Choi IY, Kim JK. A literature review and classification of recommender systems research. Expert Syst Appl 2012;39(11):10059–72. link1

[38] Cao L, Ou Y, Yu PS. Coupled behavior analysis with applications. IEEE Trans Knowl Data Eng 2012;24(8):1378–92. link1

[39] Wang C, Dong X, Zhou F, Cao L, Chi CH. Coupled attribute similarity learning on categorical data. IEEE Trans Neural Netw Learn Syst 2015;26(4):781–97. link1

[40] Wang C, Cao L, Wang M, Li J, Wei W, Ou Y. Coupled nominal similarity in unsupervised learning. In: Proceedings of the 20th ACM Conference on Information and Knowledge Management; 2011 Oct 24-28; Glasgow, UK; 2011. p. 973–8.

[41] Chen L, Zeng W, Yuan Q. A unified framework for recommending items, groups and friends in social media environment via mutual resource fusion. Expert Syst Appl 2013;40(8):2889–903. link1

[42] Nadee W. Modeling user profiles for recommender systems [dissertation]. Brisbane: Queensland University of Technology; 2016.

[43] Li R, Wang S, Deng H, Wang R, Chang KCC. Towards social user profiling: unified and discriminative influence model for inferring home locations. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2012 Aug 12-16; Beijing, China; 2012. p. 1023–31.

[44] Popescul A, Ungar LH, Pennock DM, Lawrence S. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence; 2001 Aug 2-5; Seattle, WA, USA. San Francisco: Morgan Kaufmann Publishers Inc.; 2001. p. 437–44.

[45] Chen Q, Hu L, Xu J, Liu W, Cao L. Document similarity analysis via involving both explicit and implicit semantic couplings. In: Proceedings of IEEE Data Science and Advanced Analytics 2015; 2015 Oct 19-21; Paris, France; 2015. p. 1–10.

[46] Jiang M, Cui P, Chen X, Wang F, Zhu W, Yang S. Social recommendation with cross-domain transferable knowledge. IEEE Trans Knowl Data Eng 2015;27(11):3084–97. link1

[47] Pan W, Yang Q. Transfer learning in heterogeneous collaborative filtering domains. Artif Intell 2013;197:39–55. link1

[48] Cao L. Metasynthetic computing and engineering of complex systems. London: Springer-Verlag; 2015.

[49] Son LH. Dealing with the new user cold-start problem in recommender systems: a comparative review. Inform Syst 2016;58:87–104. link1

[50] Gantner Z, Drumond L, Freudenthaler C, Rendle S, Schmidt-Thieme L. Learning attribute-to-feature mappings for cold-start recommen-dations. In: Proceedings of the 10th IEEE International Conference on Data Mining; 2010 Dec 13-17; Sydney, Australia; 2010. p. 176–85.

[51] Mirbakhsh N, Ling CX. Improving top-N recommendation for cold-start users via cross-domain information. ACM Trans Knowl Discov Data 2015;9(4):33.

[52] Lika B, Kolomvatsos K, Hadjiefthymiades S. Facing the cold start problem in recommender systems. Expert Syst Appl 2014;41(4):2065–73. link1

[53] Gao H, Tang J, Liu H. Addressing the cold-start problem in location recommendation using geo-social correlations. Data Min Knowl Disc 2015;29(2):299–323. link1

[54] Gunes I, Kaleli C, Bilge A, Polat H. Shilling attacks against recommender systems: a comprehensive survey. Artif Intell Rev 2014;42(4):767–99. link1

[55] Pang G, Cao L, Chen L. Outlier detection in complex categorical data by modelling the feature value couplings. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence 2016; 2016 Jul 9-15; New York, NY, USA; 2016. p. 1–7.

[56] Hidasi B, Tikk D. General factorization framework for context-aware recommendations. Data Min Knowl Disc 2016;30(2):342–71. link1

[57] Jacko JA, editor. The human-computer interaction handbook: fundamentals, evolving technologies and emerging applications. 3rd ed. Boca Raton: CRC Press; 2006.

[58] Qian XS, Yu JY, Dai RW. A new discipline of science—the study of open complex giant system and its methodology. Chin J Syst Eng Electron 1993;4(2):2–12.

[59] Liu X, Nielek R, Adamska P, Wierzbicki A, Aberer K. Towards a highly effective and robust Web credibility evaluation system. Decis Support Syst. 2015;79:99–108. link1

[60] Aldhahri E, Shandilya V, Shiva S. Towards an effective crowdsourcing recommendation system: a survey of the state-of-the-art. In: Proceedings of the 2015 IEEE Symposium on Service-Oriented System Engineering; 2015 Mar 30-Apr 3; San Francisco Bay, CA, USA; 2015. p. 372–7.

Related Research