Encyclopedia of Social Network Analysis and Mining

2014 Edition
| Editors: Reda Alhajj, Jon Rokne

Recommender Systems, Semantic-Based

  • Fatih Gedikli
  • Dietmar Jannach
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6170-8_116



Collaborative Filtering

A recommendation method which is based on rating information of the user community

Content-Based Filtering

A recommendation method which is based on characteristics of the recommended items as well as individual user feedback

Hybrid Recommender System

A recommender system that combines different recommendation approaches or data sources

Rating Matrix

A grid containing the users’ implicit or explicit item ratings

Cold-Start Problem

The ramp-up phase of a recommender where preference data is missing


Recommender systems (RS) are software tools that are predominantly used on e-commerce sites and for other online services as a means to help the online customer find the most relevant shopping items or pieces of information quickly. Today, such systems can be found for a variety of different domains such as books, movies, music, hotels, restaurants, or news.

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  1. Cantador I, Bellogin A, Vallet D (2010) Content-based recommendation in social tagging systems. In: Rec-Sys’10, Barcelona, pp 237–240Google Scholar
  2. Cattuto C, Benz D, Hotho A, Stumme G (2008) Semantic grounding of tag relatedness in social bookmarking systems. In: ISWC’08, Karlsruhe, pp 615–631Google Scholar
  3. de Gemmis M, Lops P, Semeraro G, Basile P (2008) Integrating tags in a semantic content-based recommender. In: RecSys’08, Lausanne, pp 163–170Google Scholar
  4. Durao F, Dolog P (2010) Extending a hybrid tag-based recommender system with personalization. In: SAC’10, Sierre, pp 1723–1727Google Scholar
  5. Firan CS, Nejdl W, Paiu R (2007) The benefit of using tag-based profiles. In: LA-WEB’07, Santiago de Chile, pp 32–41Google Scholar
  6. Gedikli F, Jannach D (2013) Improving recommendation accuracy based on item-specific tag preferences. ACM Trans Intell Syst Technol 4(1):1–19Google Scholar
  7. Gedikli F, Ge M, Jannach D (2011) Understanding recommendations by reading the clouds. In: EC-Web’11, Toulouse, pp 196–208Google Scholar
  8. Golder SA, Huberman BA (2006) Usage patterns of collaborative tagging systems. J Inf Sci 32(2):198–208Google Scholar
  9. Hotho A, Jäschke R, Schmitz C, Stumme G (2006) Information retrieval in folksonomies: search and ranking. In: ESWC’06, Budva, pp 411–426Google Scholar
  10. Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems – an introduction. Cambridge University Press, LeidenGoogle Scholar
  11. Ji A-T, Yeon C, Kim H-N, Jo G-S (2007) Collaborative tagging in recommender systems. In: AUS-AI’07, Gold Coast, pp 377–386Google Scholar
  12. Kubatz M, Gedikli F, Jannach D (2011) LocalRank – neighborhood-based, fast computation of tag recommendations. In: EC-Web’11, Toulouse, pp 258–269Google Scholar
  13. Li X, Guo L, Zhao YE (2008) Tag-based social interest discovery. In: WWW’08, Beijing, pp 675–684Google Scholar
  14. Liang H, Xu Y, Li Y (2012) Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems. In: ICDM’10, SydneyGoogle Scholar
  15. Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80Google Scholar
  16. Noll MG, Meinel C (2007) Web search personalization via social bookmarking and tagging. In: ISWC’07/ASWC’07, Busan, pp 367–380Google Scholar
  17. Passant A (2007) Using ontologies to strengthen folk-sonomies and enrich information retrieval in weblogs. In: ICWSM’07, BoulderGoogle Scholar
  18. Pitkow J, Schütze H, Cass T, Cooley R, Turnbull D, Edmonds A, Adar E, Breuel T (2002) Personalized search. Commun ACM 45(9):50–55Google Scholar
  19. Rendle S, Schmidt-Thieme L (2010) Pairwise interaction tensor factorization for personalized tag recommendation. In: WSDM’10, New York, pp 81–90Google Scholar
  20. Rendle S, Balby Marinho L, Nanopoulos A, Lars S-T (2009) Learning optimal ranking with tensor factorization for tag recommendation. In: SIGKDD’09, Paris, pp 727–736Google Scholar
  21. Sen S, Vig J, Riedl JT (2009) Tagommenders: connecting users to items through tags. In: WWW’09, Madrid, pp 671–680Google Scholar
  22. Seth A, Zhang J (2008) A social network based approach to personalized recommendation of participatory media content. In: ICWSM’08, SeattleGoogle Scholar
  23. Shepitsen A, Gemmell J, Mobasher B, Burke R (2008) Personalized recommendation in social tagging systems using hierarchical clustering. In: RecSys’08, Lausanne, pp 259–266Google Scholar
  24. Symeonidis P, Nanopoulos A, Manolopoulos Y (2008) Tag recommendations based on tensor dimensionality reduction. In: RecSys’08, Lausanne, pp 43–50Google Scholar
  25. Tso-Sutter KHL, Marinho LB, Schmidt-Thieme L (2008) Tag-aware recommender systems by fusion of collaborative filtering algorithms. In: SAC’08, Fortaleza, pp 1995–1999Google Scholar
  26. Vatturi PK, Geyer W, Dugan C, Muller M, Brownholtz B (2008) Tag-based filtering for personalized bookmark recommendations. In: CIKM’08, Napa Valley, pp 1395–1396Google Scholar
  27. Vig J, Sen S, Riedl JT (2009) Tagsplanations: explaining recommendations using tags. In: IUI’09, Sanibel Island, pp 47–56Google Scholar
  28. Vig J, Soukup M, Sen S, Riedl JT (2010) Tag expression: tagging with feeling. In: UIST’10, New York, pp 323–332Google Scholar
  29. Xu G, Gu Y, Dolog P, Zhang Y, Kitsuregawa M (2011a) Semrec: A semantic enhancement framework for tag based recommendation. In: AAAI’11, San Francisco, pp 1267–1272Google Scholar
  30. Xu G, Gu Y, Zhang Y, Yang Z, Kitsuregawa M (2011b) Toast: a topic-oriented tag-based recommender system. In: WISE’11, Sydney, pp 158–171Google Scholar
  31. Zanardi V, Capra L (2011) A scalable tag-based recommender system for new users of the Social Web. In: DEXA’11, Toulouse, pp 542–557Google Scholar
  32. Zhen Y, Li W-J, Yeung D-Y (2009) Tagicofi: tag informed collaborative filtering. In: RecSys’09, New York, pp 69–76Google Scholar

Recommended Reading

  1. Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems – an introduction. Cambridge University Press, LeidenGoogle Scholar
  2. Ricci F, Rokach L, Shapira B, Kantor PB (eds) (2011) Recommender systems handbook. Springer, New YorkzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Fatih Gedikli
    • 1
  • Dietmar Jannach
    • 1
  1. 1.Department of Computer Science, TU DortmundDortmundGermany