Synonyms
Glossary
- Context:
-
Situational factors influencing the evaluation of a user for an item
- Experience:
-
The interaction of a user with an item that is resulting in an evaluation
- Evaluation Prediction:
-
The system’s prediction of the user’s evaluation for an item
- Information Filtering:
-
Technique for providing only relevant information to a user
- Item:
-
Information content that can be recommended by a RS
- Personalization:
-
Providing a user with content adapted or suited to their needs and wants
- Preferences:
-
A structured representation of the user preferences for items
- Recommendations:
-
System’s selected items that are suggested to a user
- RSs:
-
Recommender systems
- Situation:
-
Conditions under which an item is evaluated by a user
- Tag:
-
Metadata in the form of freely chosen keyword
Definition
RSs are information search and filtering tools that provide suggestions for items to be of use to a user. They have become common in a large number of Internet...
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Adomavicius G, Kwon Y (2012) Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans Knowl Data Eng 24(5):896–911
Adomavicius G, Tuzhilin A (2005) 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
Adomavicius G, Sankaranarayanan R, Sen S, Tuzhilin A (2005) Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans Inf Syst 23(1):103–145
Adomavicius G, Mobasher B, Ricci F, Tuzhilin A (2011) Context-aware recommender systems. AI Mag 32(3):67–80
Baccigalupo C, Plaza E (2006) Case-based sequential ordering of songs for playlist recommendation. In: Roth-Berghofer T, Göker MH, Güvenir HA (eds) Advances in case-based reasoning, Proceedings of the 8th European conference on case-based reasoning, ECCBR 2006, Fethiye. Lecture notes in computer science, vol 4106. Springer, pp 286–300
Baltrunas L, Ludwig B, Peer S, Ricci F (2012) Context relevance assessment and exploitation in mobile recommender systems. Personal Ubiquitous Comput 16(5):507–526
Berkovsky S, Kuflik T, Ricci F (2008) Mediation of user models for enhanced personalization in recommender systems. User Model User Adapt Interact 18(3):245–286
Bridge D, Göker M, McGinty L, Smyth B (2006) Case-based recommender systems. Knowl Eng Rev 20(3):315–320
Burke R (2007) Hybrid web recommender systems. In: The adaptive web. Springer, Berlin/Heidelberg, pp 377–408
Burke RD, O’Mahony MP, Hurley NJ (2011) Robust collaborative recommendation. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, New York, pp 805–835
Desrosiers C, Karypis G (2011) A comprehensive survey of neighborhood-based recommendation methods. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, New York, pp 107–144
Elahi M, Repsys V, Ricci F (2011) Rating elicitation strategies for collaborative filtering. In: Proceedings of the E-commerce and web technologies – 12th international conference, EC-Web, Toulouse, 30 Aug–1 Sept 2011. Lecture notes in business information processing, vol 85. Springer, pp 160–171
Golbandi N, Koren Y, Lempel R (2011) Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the forth international conference on web search and web data mining, WSDM, Hong Kong, 9–12 Feb 2011, pp 595–604
Golbeck J (2006) Generating predictive movie recommendations from trust in social networks. In: Proceedings of the trust management, 4th international conference, iTrust, Pisa, 16–19 May 2006, pp 93–104
Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70
Harpale A, Yang Y (2008) Personalized active learning for collaborative filtering. In: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval, SIGIR, Singapore, 20–24 July 2008. ACM, pp 91–98
Jameson A, Smyth B (2007) Recommendation to groups. In: The adaptive web. Springer, Berlin, pp 596–627
Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction. Cambridge University Press, New York
Kobsa A (2007) Generic user modeling systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web, Lecture notes in computer science, vol 4321. Springer, Berlin, pp 136–154
Kobsa A (2008) Privacy-enhanced personalization. In: Proceedings of the twenty-first international Florida artificial intelligence research society conference, Coconut Grove, 15–17 May 2008. AAAI, p 10
Konstan JA, Riedl J (2012) Recommender systems: from algorithms to user experience. User Model User Adapt Interact 22(1–2):101–123
Koren Y, Bell R (2011) Advances in collaborative filtering. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, New York, pp 145–186
Kuflik T, Wecker AJ, Cena F, Gena C (2012) Evaluating rating scales personality. In: Proceedings of the user modeling, adaptation, and personalization – 20th international conference, UMAP, Montreal, 16–20 July 2012, pp 310–315
Lops P, de Gemmis M, Semeraro G (2011) Content-based recommender systems: state of the art and trends. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, New York, pp 73–105
Mahmood T, Ricci F, Venturini A (2009) Improving recommendation effectiveness by adapting the dialogue strategy in online travel planning. Int J Inf Technol Tour 11(4):285–302
Marinho LB, Nanopoulos A, Schmidt-Thieme L, Jäschke R, Hotho A, Stumme G, Symeonidis P (2011) Social tagging recommender systems. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, New York, pp 215–644
Masthoff J (2011) Group recommender systems: combining individual models. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, New York, pp 677–702
McGinty L, Reilly J (2011) On the evolution of critiquing recommenders. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, New York, pp 419–453
Moling O, Baltrunas L, Ricci F (2012) Optimal radio channel recommendations with explicit and implicit feedback. In: RecSys’12: Proceedings of the 2012 ACM conference on recommender systems, Dublin, pp 75–82
Rashid AM, Karypis G, Riedl J (2008) Learning preferences of new users in recommender systems: an information theoretic approach. SIGKDD Explor Newsl 10:90–100
Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings ACM conference on computer-supported cooperative work, Chapel Hill, pp 175–186
Ricci F (2011) Mobile recommender systems. Int J Inf Technol Tour 12(3):205–231
Ricci F, Rokach L, Shapira B, Kantor PB (2011) Recommender systems handbook. Springer, New York
Rubens N, Kaplan D, Sugiyama M (2011) Active learning in recommender systems. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, New York, pp 735–767
Schwartz B (2004) The paradox of choice. ECCO, New York
Senot C, Kostadinov D, Bouzid M, Picault J, Aghasaryan A, Bernier C (2010) Analysis of strategies for building group profiles. In: Proceedings of the user modeling, adaptation, and personalization, 18th international conference, UMAP, Big Island, 20–24 June 2010. Springer, pp 40–51
Shani G, Heckerman D, Brafman RI (2005) An mdp-based recommender system. J Mach Learn Res 6:1265–1295
Smyth B, McClave P (2001) Similarity vs diversity. In: Case-based reasoning research and development, Proceedings of the 4th international conference on case-based reasoning, ICCBR 2001, Vancouver. Springer
Tintarev N, Masthoff J (2012) Evaluating the effectiveness of explanations for recommender systems – methodological issues and empirical studies on the impact of personalization. User Model User Adapt Interact 22(4–5):399–439
Vargas S, Castells P (2011) Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the 2011 ACM conference on recommender systems, RecSys’11, Chicago, 23–27 Oct 2011. ACM, pp 109–116
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media LLC, part of Springer Nature
About this entry
Cite this entry
Ricci, F. (2018). Recommender Systems: Models and Techniques. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_88
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7131-2_88
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-7130-5
Online ISBN: 978-1-4939-7131-2
eBook Packages: Computer ScienceReference Module Computer Science and Engineering