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Recommender Systems Handbook pp 1–35Cite as

Recommender Systems: Techniques, Applications, and Challenges

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Abstract

Recommender systems (RSs) are software tools and techniques that provide suggestions for items that are most likely of interest to a particular user. In this introductory chapter, we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters in this handbook. Additionally, we aim to help the reader navigate the rich and detailed content that this handbook offers.

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References

  1. G. Adomavicius, K. Bauman, B. Mobasher, F. Ricci, A. Tuzhilin, M. Unger, Workshop on context-aware recommender systems, in RecSys 2020: Fourteenth ACM Conference on Recommender Systems, Virtual Event, Brazil, September 22–26, 2020, ed. by R.L.T. Santos, L.B. Marinho, E.M. Daly, L. Chen, K. Falk, N. Koenigstein, E.S. de Moura (ACM, New York, 2020), pp. 635–637

    Google Scholar 

  2. G. Adomavicius, A. Tuzhilin, Personalization technologies: a process-oriented perspective. Commun. ACM 48(10), 83–90 (2005)

    Article  Google Scholar 

  3. G. Adomavicius, A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  4. C.C. Aggarwal, Recommender Systems - The Textbook (Springer, New York, 2016)

    Book  Google Scholar 

  5. X. Amatriain, Mining large streams of user data for personalized recommendations. SIGKDD Explor. Newsl. 14(2), 37–48 (2013)

    Article  Google Scholar 

  6. X. Amatriain, J. Basilico, Past, present, and future of recommender systems: an industry perspective, in Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15–19, 2016, ed. by S. Sen, W. Geyer, J. Freyne, P. Castells (ACM, New York, 2016), pp. 211–214

    Google Scholar 

  7. O. Arazy, N. Kumar, B. Shapira, Improving social recommender systems. IT Prof. 11(4), 38–44 (2009)

    Article  Google Scholar 

  8. H. Asoh, C. Ono, Y. Habu, H. Takasaki, T. Takenaka, Y. Motomura, An acceptance model of recommender systems based on a large-scale internet survey, in Advances in User Modeling - UMAP 2011 Workshops, Girona, July 11–15, 2011, Revised Selected Papers (2011), pp. 410–414

    Google Scholar 

  9. R.A. Bailey, Design of Comparative Experiments (Cambridge University Press, Cambridge, 2008)

    Book  MATH  Google Scholar 

  10. M. Balabanovic, Y. Shoham, Content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)

    Article  Google Scholar 

  11. L. Baltrunas, F. Ricci, Experimental evaluation of context-dependent collaborative filtering using item splitting. User Model. User-Adapt. Interact. 24(1–2), 7–34 (2014)

    Article  Google Scholar 

  12. D. Ben-Shimon, A. Tsikinovsky, L. Rokach, A. Meisels, G. Shani, L. Naamani, Recommender system from personal social networks, in AWIC, Advances in Soft Computing, vol. 43, ed. by K. Wegrzyn-Wolska, P.S. Szczepaniak (Springer, New York, 2007), pp. 47–55

    Google Scholar 

  13. S. Berkovsky, T. Kuflik, F. Ricci, Mediation of user models for enhanced personalization in recommender systems. User Model. User-Adapted Interact. 18(3), 245–286 (2008)

    Article  Google Scholar 

  14. S. Berkovsky, T. Kuflik, F. Ricci, Cross-representation mediation of user models. User Model. User-Adapted Interact. 19(1–2), 35–63 (2009)

    Article  Google Scholar 

  15. B. Biggio, I. Corona, B. Nelson, B.I., Rubinstein, D. Maiorca, G. Fumera, G. Giacinto, F. Roli, Security evaluation of support vector machines in adversarial environments, in Support Vector Machines Applications (Springer, New York, 2014), pp. 105–153

    Google Scholar 

  16. D. Billsus, M. Pazzani, Learning probabilistic user models, in UM97 Workshop on Machine Learning for User Modeling (1997). http://www.dfki.de/bauer/um-ws/

  17. J. Bobadilla, F. Ortega, A. Hernando, A. Gutierrez, Recommender systems survey. Knowl. Based Syst. 46(0), 109–132 (2013)

    Article  Google Scholar 

  18. J. Borràs, A. Moreno, A. Valls, Intelligent tourism recommender systems: a survey. Expert Syst. Appl. 41(16), 7370–7389 (2014)

    Article  Google Scholar 

  19. D. Bridge, M. Göker, L. McGinty, B. Smyth, Case-based recommender systems. Knowl. Eng. Rev. 20(3), 315–320 (2006)

    Article  Google Scholar 

  20. P. Brusilovsky, Methods and techniques of adaptive hypermedia. User Model. User-Adapted Interact. 6(2–3), 87–129 (1996)

    Article  MATH  Google Scholar 

  21. R. Burke, Hybrid web recommender systems, in The Adaptive Web (Springer, Berlin, 2007), pp. 377–408

    Google Scholar 

  22. M. Chelliah, S. Sarkar, Product recommendations enhanced with reviews, in Proceedings of the Eleventh ACM Conference on Recommender Systems (2017), pp. 398–399

    Google Scholar 

  23. L. Chen, M. de Gemmis, A. Felfernig, P. Lops, F. Ricci, G. Semeraro, Human decision making and recommender systems. TiiS 3(3), 17 (2013)

    Google Scholar 

  24. L. Chen, P. Pu, Critiquing-based recommenders: survey and emerging trends. User Model. User-Adapt. Interact. 22(1–2), 125–150 (2012)

    Article  Google Scholar 

  25. D. Cosley, S.K., Lam, I., Albert, J.A., Konstant, J. Riedl, Is seeing believing? How recommender system interfaces affect users’ opinions, in Proceedings of the CHI 2003 Conference on Human factors in Computing Systems, Fort Lauderdale, FL (2003), pp. 585–592

    Google Scholar 

  26. M.D. Ekstrand, F.M. Harper, M.C. Willemsen, J.A. Konstan, User perception of differences in recommender algorithms, in Eighth ACM Conference on Recommender Systems, RecSys ’14, Foster City, Silicon Valley, CA, 06–10 Oct 2014, pp. 161–168

    Google Scholar 

  27. K. Falk, Practical Recommender Systems (Manning Publications, Shelter Island, 2019)

    Google Scholar 

  28. C. Feely, B. Caulfield, A. Lawlor, B. Smyth, Using case-based reasoning to predict marathon performance and recommend tailored training plans, in Case-Based Reasoning Research and Development - 28th International Conference, ICCBR 2020, Salamanca, Spain, June 8–12, 2020, ed. by I. Watson, R.O. Weber. Lecture Notes in Computer Science, vol. 12311 (Springer, New York, 2020), pp. 67–81

    Google Scholar 

  29. A. Felfernig, N. Tintarev, T.N.T. Trang, M. Stettinger, Designing explanations for group recommender systems. CoRR abs/2102.12413 (2021)

    Google Scholar 

  30. G. Fisher, User modeling in human-computer interaction. User Model. User-Adapted Interact. 11, 65–86 (2001)

    Article  Google Scholar 

  31. J. Golbeck, Generating predictive movie recommendations from trust in social networks, in Trust Management, 4th International Conference, iTrust 2006 Proceedings, Pisa, 16–19 May 2006, pp. 93–104

    Google Scholar 

  32. D. Goldberg, D. Nichols, B.M. Oki, D. Terry, Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)

    Article  Google Scholar 

  33. S.K. Gorakala, M. Usuelli, Building a Recommendation System with R (Packt Publishing Ltd., Birmingham, 2015)

    Google Scholar 

  34. N. Hazrati, M. Elahi, F. Ricci, Simulating the impact of recommender systems on the evolution of collective users’ choices, in HT ’20: 31st ACM Conference on Hypertext and Social Media, Virtual Event, 13–15 July 2020, ed. by U. Gadiraju (ACM, New York, 2020), pp. 207–212

    Google Scholar 

  35. J. Herlocker, J. Konstan, J. Riedl, Explaining collaborative filtering recommendations, in Proceedings of ACM 2000 Conference on Computer Supported Cooperative Work (2000), pp. 241–250

    Google Scholar 

  36. J.L. Herlocker, J.A. Konstan, L.G. Terveen, J.T. Riedl, Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)

    Article  Google Scholar 

  37. A. Jameson, Recommender systems seen through the lens of choice architecture, in Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2015, Co-located with ACM Conference on Recommender Systems (RecSys 2015), Vienna, 19 Sept 2015, CEUR Workshop Proceedings, vol. 1438, ed. by J. O’Donovan, A. Felfernig, N. Tintarev, P. Brusilovsky, G. Semeraro, P. Lops (2015), p. 1. CEUR-WS.org

  38. D. Jannach, M. Zanker, A. Felfernig, G. Friedrich, Recommender Systems: An Introduction (Cambridge University Press, Cambridge, 2010)

    Book  Google Scholar 

  39. J.L. Jorro-Aragoneses, M. Caro-Martínez, J.A. Recio-García, B. Díaz-Agudo, G. Jiménez-Díaz, Personalized case-based explanation of matrix factorization recommendations, in Case-Based Reasoning Research and Development - 27th International Conference, ICCBR 2019, Otzenhausen, Germany, 8–12 Sept 2019, Proceedings, vol. 11680, ed. by K. Bach, C. Marling. Lecture Notes in Computer Science (Springer, New York, 2019), pp. 140–154

    Google Scholar 

  40. J.A. Konstan, J. Riedl, Recommender systems: from algorithms to user experience. User Model. User-Adapted Interact. 22(1–2), 101–123 (2012)

    Article  Google Scholar 

  41. Y. Koren, R.M. Bell, C. Volinsky, Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)

    Article  Google Scholar 

  42. G. Linden, B. Smith, J. York, Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

  43. P. Lops, M. de Gemmis, G. Semeraro, Content-based recommender systems: state of the art and trends, in Recommender Systems Handbook, ed. by F. Ricci, L. Rokach, B. Shapira, P.B. Kantor (Springer, New York, 2011), pp. 73–105

    Chapter  Google Scholar 

  44. L. Lu, M. Medo, C.H. Yeung, Y.C. Zhang, Z.K. Zhang, T. Zhou, Recommender systems. Phys. Rep. 519(1), 1–49 (2012)

    Article  Google Scholar 

  45. T. Mahmood, F. Ricci, A. Venturini, Learning adaptive recommendation strategies for online travel planning, in Information and Communication Technologies in Tourism 2009 (Springer, New York, 2009), pp. 149–160

    Google Scholar 

  46. D. Massimo, F. Ricci, Clustering users’ pois visit trajectories for next-poi recommendation, in Information and Communication Technologies in Tourism 2019, ENTER 2019, Proceedings of the International Conference in Nicosia, Cyprus, 30 Jan–1 Feb 2019, ed. by J. Pesonen, J. Neidhardt (Springer, New York, 2019), pp. 3–14

    Google Scholar 

  47. D. Massimo, F. Ricci, Enhancing travel experience leveraging on-line and off-line users’ behaviour data, in IUI ’20: 25th International Conference on Intelligent User Interfaces, Cagliari, 17–20 March 2020, Companion (ACM, New York, 2020), pp. 65–66

    Google Scholar 

  48. J. McAuley, C. Targett, Q. Shi, A. Van Den Hengel, Image-based recommendations on styles and substitutes, in Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (2015), pp. 43–52

    Google Scholar 

  49. B. McFee, T. Bertin-Mahieux, D.P. Ellis, G.R. Lanckriet, The million song dataset challenge, in Proceedings of the 21st International Conference Companion on World Wide Web, WWW ’12 Companion (ACM, New York, 2012), pp. 909–916

    Book  Google Scholar 

  50. S.M. McNee, J. Riedl, J.A. Konstan, Being accurate is not enough: how accuracy metrics have hurt recommender systems, in CHI ’06: CHI ’06 Extended Abstracts on Human Factors in Computing Systems (ACM Press, New York, 2006), pp. 1097–1101

    Google Scholar 

  51. M. Montaner, B. López, J.L. de la Rosa, A taxonomy of recommender agents on the internet. Artif. Intell. Rev. 19(4), 285–330 (2003)

    Article  Google Scholar 

  52. M. Otsuka, T. Osogami, A deep choice model, in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 12–17 Feb 2016, Phoenix, Arizona, ed. by D. Schuurmans, M.P. Wellman (AAAI Press, Menlo Park, 2016), pp. 850–856

    Google Scholar 

  53. T.K. Paradarami, n.d. Bastian, J.L. Wightman, A hybrid recommender system using artificial neural networks. Expert Syst. Appl. 83, 300–313 (2017)

    Google Scholar 

  54. D.H. Park, H.K. Kim, I.Y. Choi, J.K. Kim, A literature review and classification of recommender systems research. Expert Syst. Appl. 39(11), 10059–10072 (2012)

    Article  Google Scholar 

  55. M. Perano, G.L. Casali, Y. Liu, T. Abbate, Professional reviews as service: a mix method approach to assess the value of recommender systems in the entertainment industry. Technol. Forecast. Soc. Change 169, 120800 (2021)

    Article  Google Scholar 

  56. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, J. Riedl, Grouplens: an open architecture for collaborative filtering of netnews, in Proceedings ACM Conference on Computer-Supported Cooperative Work (1994), pp. 175–186

    Google Scholar 

  57. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, J. Riedl, Grouplens: an open architecture for collaborative filtering of netnews, in Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (1994), pp. 175–186

    Google Scholar 

  58. P. Resnick, H.R. Varian, Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  59. M. Reusens, W. Lemahieu, B. Baesens, L. Sels, A note on explicit versus implicit information for job recommendation. Decis. Support Syst. 98, 26–35 (2017)

    Article  Google Scholar 

  60. F. Ricci, Travel recommender systems. IEEE Intell. Syst. 17(6), 55–57 (2002)

    Google Scholar 

  61. F. Ricci, Recommender systems: models and techniques, in Encyclopedia of Social Network Analysis and Mining, ed. by R. Alhajj, J.G. Rokne, 2nd edn. (Springer, New York, 2018)

    Google Scholar 

  62. F. Ricci, D. Cavada, N. Mirzadeh, A. Venturini, Case-based travel recommendations, in Destination Recommendation Systems: Behavioural Foundations and Applications, ed. by D.R. Fesenmaier, K. Woeber, H. Werthner (CABI, Wallingford, 2006), pp. 67–93

    Chapter  Google Scholar 

  63. J.B. Schafer, D. Frankowski, J. Herlocker, S. Sen, Collaborative filtering recommender systems, in The Adaptive Web (Springer, Berlin, 2007), pp. 291–324

    Google Scholar 

  64. J.B. Schafer, J.A. Konstan, J. Riedl, E-commerce recommendation applications. Data Min. Knowl. Disc. 5(1/2), 115–153 (2001)

    Article  MATH  Google Scholar 

  65. M. Schrage, Recommendation Engines (MIT Press, Cambridge, 2020)

    Book  Google Scholar 

  66. B. Schwartz, The Paradox of Choice (ECCO, New York, 2004)

    Google Scholar 

  67. M. van Setten, S.M. McNee, J.A. Konstan, Beyond personalization: the next stage of recommender systems research, in IUI, ed. by R.S. Amant, J. Riedl, A. Jameson (ACM, New York, 2005), p. 8

    Google Scholar 

  68. U. Shardanand, P. Maes, Social information filtering: algorithms for automating “word of mouth”, in Proceedings of the Conference on Human Factors in Computing Systems (CHI’95) (1995), pp. 210–217

    Google Scholar 

  69. R.R. Sinha, K. Swearingen, Comparing recommendations made by online systems and friends, in DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries (2001)

    Google Scholar 

  70. B. Smith, G. Linden, Two decades of recommender systems at amazon.com. IEEE Internet Comput. 21(3), 12–18 (2017)

    Google Scholar 

  71. K. Swearingen, R. Sinha, Beyond algorithms: an HCI perspective on recommender systems, in Recommender Systems, papers from the 2001 ACM SIGIR Workshop, New Orleans, LA, ed. by J.L. Herlocker (2001)

    Google Scholar 

  72. T.N.T. Tran, M. Atas, A. Felfernig, V.M. Le, R. Samer, M. Stettinger, Towards social choice-based explanations in group recommender systems, in Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019, Larnaca, Cyprus, 9–12 June 2019, ed. by G.A. Papadopoulos, G. Samaras, S. Weibelzahl, D. Jannach, O.C. Santos (ACM, New York, 2019), pp. 13–21

    Google Scholar 

  73. S. Zhang, L. Yao, A. Sun, Y. Tay, Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 52(1), 1–38 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

  2. Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beersheba, Israel

    Lior Rokach & Bracha Shapira

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  1. Francesco Ricci
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  2. Lior Rokach
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  3. Bracha Shapira
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Correspondence to Francesco Ricci .

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Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

  2. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Ricci, F., Rokach, L., Shapira, B. (2022). Recommender Systems: Techniques, Applications, and Challenges. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2197-4_1

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