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Recommender systems — beyond matrix completion

Published:28 October 2016Publication History
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Abstract

The future success of these systems depends on more than a Netflix challenge.

References

  1. Adomavicius, G. and Tuzhilin, A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge and Data Engineering 17, 6 (2005), 734--749. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Adomavicius, G. and Tuzhilin, A. Context-aware recommender systems. Recommender Systems Handbook. Springer, 2011, 217--253.Google ScholarGoogle Scholar
  3. Billsus, D. and Pazzani, M.J. Learning collaborative information filters. In Proceedings ICML '98 (1998), 46--54. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Breese, J.S., Heckerman, D. and Kadie, C.M. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings UAI '98 (1998), 43--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Castells, P., Wang, J., Lara, R. and Zhang, D. Introduction to the special issue on diversity and discovery in recommender systems. ACM Trans. Intell. Syst. Technology 5, 4 (2014), 52:1--52:3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Chau, P.Y.K., Ho, S.Y., Ho, K.K.W. and Yao, Y. Examining the effects of malfunctioning personalized services on online users' distrust and behaviors. Decision Support Syst. 56 (2013), 180--191.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Cremonesi, P., Garzotto, F. and Turrin, R. Investigating the persuasion potential of recommender systems from a quality perspective: An empirical study. ACM Trans. Interact. Intell. Syst. 2, 1 (2012), 11:1--11:41. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Denning, P.J. ACM president's letter: Electronic junk. Commun. ACM 25, 3 (Mar. 1982), 163--165. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Dias, M.B., Locher, D., Li, M., El-Deredy, W. and Lisboa, P.J. The value of personalised recommender systems to e-business: A case study. In Proceedings RecSys'08 (2008), 291--294. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Felfernig, A., Friedrich, G., Jannach, D. and Zanker, M. An integrated environment for the development of knowledge-based recommender applications. Int. J. Electron. Commerce 11, 2 (2006), 11--34. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Friedrich, G. and Zanker, M. A taxonomy for generating explanations in recommender systems. AI Magazine 32, 3 (2011), 90--98.Google ScholarGoogle ScholarCross RefCross Ref
  12. Garcin, F., Faltings, B. Donatsch, O., Alazzawi, A., Bruttin, C. and Huber, A. Offline and online evaluation of news recommender systems at swissinfo.ch. In Proceedings RecSys '14 (2014), 169--176. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Ghose, A., Ipeirotis, P.G. and Li, B. Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science 31, 3 (2012), 493--520. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Goldberg, D., Nichols, D., Oki, B. and Terry, D. Using collaborative filtering to weave an information tapestry. Commun. ACM (1992), 61--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Gomez-Uribe, C.A. and Hunt, N. The Netflix Recommender System: Algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst. 6, 4 (2015), 13:1--13:19. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Gorgoglione, M., Panniello, U. and Tuzhilin, A. The effect of context-aware recommendations on customer purchasing behavior and trust. In Proceedings RecSys '11 (2011), 85--92. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Hensley, C.B. Selective dissemination of information (SDI): State of the art in May, 1963. In Proceedings of AFIPS '63 (Spring), 1963, 257--262. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Hill, W., Stead, L., Rosenstein, M. and Furnas, G. Recommending and evaluating choices in a virtual community of use. In Proceedings CHI '95 (1995), 194--201. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Jannach, D., Lerche, L., Kamehkhosh, I. and Jugovac, M. What recommenders recommend: An analysis of recommendation biases and possible countermeasures. User Modeling and User-Adapted Interaction (2015), 25:1--65. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Jindal, N. and Liu, B. Opinion spam and analysis. In Proceedings WSDM '08, (2008), 219--230. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Konstan, J. and Riedl, J. Recommender systems: From algorithms to user experience. User Modeling and User-Adapted Interaction 22, 1--2 (2012), 101--123. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Lam, S.K. and Riedl, J. Shilling recommender systems for fun and profit. In Proceedings of WWW '04, (2004), 393--402. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Linden, G., Smith, B. and York, J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7, 1 (2003), 76--80. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Mahmood, T., Ricci, F. and Venturini, A. Improving recommendation effectiveness: Adapting a dialogue strategy in online travel planning. J. of IT & Tourism 11, 4 (2009), 285--302.Google ScholarGoogle Scholar
  25. Malone, T.W., Grant, K.R., Turbak, F.A., Brobst, S.A. and Cohen, M.D. Intelligent information-sharing systems. Commun. ACM 30, 5 (May 1987), 390--402. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Marlin, B.M. and Zemel, R.S. Collaborative prediction and ranking with non-random missing data. In Proceedings RecSys '09 (2009), 5--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. McGinty, L. and Reilly, J. On the evolution of critiquing recommenders. Recommender Systems Handbook, Springer, 2011, 419--453.Google ScholarGoogle Scholar
  28. McNee, S.M., Riedl, J. and Konstan, J.A. Being accurate is not enough: How accuracy metrics have hurt recommender systems. In Proceedings CHI '06, (2006), 1097--1101. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Mobasher, B., Burke, R., Bhaumik, R. and Williams, C. Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Technology 7, 4 (Oct. 2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Neidhardt, J., Seyfang, L., Schuster, R. and Werthner, H. A picture-based approach to recommender systems. J. of IT & Tourism 15 (2015), 1--21.Google ScholarGoogle Scholar
  31. Panniello, U., Tuzhilin, A. and Gorgoglione, M. Comparing context-aware recommender systems in terms of accuracy and diversity. User Modeling and User-Adapted Interaction 24, 1-2 (2014), 35--65. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Papadimitriou, A., Symeonidis, P. and Manolopoulos, Y. A generalized taxonomy of explanations styles for traditional and social recommender systems. Data Min. Knowl. Discovery 24, 3 (2012), 555--583. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. and Riedl, J. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of CSCW'94 (1994), 175--186. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Resnick, P. and Sami, R. The information cost of manipulation-resistance in recommender systems. In Proceedings RecSys '08 (2008), 147--154. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Schafer, J.B., Konstan, J. and Riedl, J. Recommender systems in e-commerce. In Proceedings ACM EC '99 (1999), 158--166. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Shardanand, U. and Maes, P. Social information filtering: Algorithms for automating "word of mouth." In Proceedings CHI '95 (1995), 210--217. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Shimazu, H. Expertclerk: Navigating shoppers' buying process with the combination of asking and proposing. In Proceedings IJCAI '01 (2001), 1443--1448. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Wagstaff, K. Machine learning that matters. In Proceedings ICML (2012), 529--536.Google ScholarGoogle Scholar
  39. Xiao, B. and Benbasat, I. E-commerce product recommendation agents: Use, characteristics, and impact. MIS Q. 31, 1 (Mar. 2007), 137--209. Google ScholarGoogle ScholarDigital LibraryDigital Library

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            cover image Communications of the ACM
            Communications of the ACM  Volume 59, Issue 11
            November 2016
            118 pages
            ISSN:0001-0782
            EISSN:1557-7317
            DOI:10.1145/3013530
            • Editor:
            • Moshe Y. Vardi
            Issue’s Table of Contents

            Copyright © 2016 ACM

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            Publication History

            • Published: 28 October 2016

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