Abstract
In this chapter, the two most widely used types of recommender systems, namely the collaborative filtering method and the content-based system, along with a few of their important sub-types are discussed in this chapter. There are two types of collaborative methods, namely the neighborhood-based and model-based methods. The chapter discusses what are the features of and differences between the two methods. The basic components of the content-based systems are also discussed. Both the systems have their advantages and disadvantages which are also discussed here.
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References
Aslanian E, Radmanesh M, Jalili M (2016) Hybrid recommender systems based on content feature relationship. IEEE Transactions on Industrial Informatics
Beel J, Genzmehr M, Langer S, Nürnberger A, Gipp B (2013) A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation.In: Proceedings of the international workshop on reproducibility and replication in recommender systems evaluation. ACM, pp 7–14
Bellogin A, Castells P, Cantador I (2011) Precision-oriented evaluation of recommender systems: an algorithmic comparison. In: Proceedings of RECSYS. ACM, pp 333–336
Bobadilla J, Ortega F, Hernando A, Alcalá J (2011) Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowl Based Syst 24(8):1310–1316
Bogers T, Van den Bosch A (2008) Recommending scientific articles using citeulike. In: Proceedings of RECSYS. ACM, pp 287–290
Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12(4):331–370
Chowdhury G (2010) Introduction to modern information retrieval. Facet Publishing, Abingdon
Das AS, Datar M, Garg A, Rajaram S (2007) Google news personalization: scalable online collaborative filtering. In: Proceedings of 7 WWW. ACM, pp 271–280
Di Noia T, Mirizzi R, Ostuni VC, Romito D, Zanker M (2012.) Linked open data to support content-based recommender systems. In: Proceedings of Semantics. ACM, pp 1–8
Fernandes BB, Sacenti JA, Willrich R (2017) Using implicit feedback for neighbors selection: alleviating the sparsity problem in collaborative recommendation systems. In: Proceedings of WEBMEDIA. ACM, pp 341–348
Ge M, Delgado-Battenfeld C, Jannach D (2010) Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of RECSYS. ACM, pp 257–260
Glauber R, Loula A, Rocha-Junior JB (2013) A mixed hybrid recommender system for given names. ECML PKDD Discov Challenge 2013:25–36
Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70
Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53
Konstan J, Riedl J (2012) Recommender systems: from algorithms to user experience. User Model User-Adapt Interact 22(1):101–123
Lathia N, Hailes S, Capra L, Amatriain X (2010) Temporal diversity in recommender systems. In: Proceedings of ACM SIGIR. ACM, pp 210–217
Liu Y, Wang S, Khan MS, He J (2018) A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Mining and Analytics 1(3):211–221
McNee SM, Riedl J, Konstan JA (2006) Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Proceedings of CHI. ACM, pp 1097–1101
Said A, Bellogín A (2014) Comparative recommender system evaluation: benchmarking recommendation frameworks. In: Proceedings of RECSYS. ACM, pp 129–136
Santana LL, Souza AB, Santana DL, Dourado WA, Durão FA (2017) Evaluating ensemble strategies for recommender systems under metadata reduction. In: Proceedings of WEBMEDIA. ACM, pp 125–132
Shani G, Gunawardana A (2011) Evaluating recommendation systems. In: Recommender systems handbook. Springer, Berlin, pp 257–297
Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of SIGCHI. ACM Press/Addison-Wesley Publishing Co., Boston, MA, pp 210–217
Silva DV, Silva RD, Durão FA (2017a) RecStore: recommending stores for shopping mall customers. In: Proceedings of WEBMEDIA. ACM, pp 117–124
Silva N, Carvalho D, Pereira AC, Mourão F, Rocha L (2017b) Evaluating different strategies to mitigate the ramp-up problem in recommendation domains. In: Proceedings of WEBMEDIA. ACM, pp 333–340
Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:4
Wu D, Zhang G, Lu J (2015) A fuzzy preference treebased recommender system for personalized business-to-business e-services. IEEE Trans Fuzzy Syst 23(1):29–43
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Kar, P., Roy, M., Datta, S. (2024). Collaborative Filtering and Content-Based Systems. In: Recommender Systems: Algorithms and their Applications. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-97-0538-2_3
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DOI: https://doi.org/10.1007/978-981-97-0538-2_3
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