Glossary
- Cold start problem:
-
The ramp-up phase of a recommender where preference data is missing
- 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 the 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 rating
Definition
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...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cantador I, Bellogin A, Vallet D (2010) Content-based recommendation in social tagging systems. In: Rec-Sys’10, Barcelona, pp 237–240
Covington P, Adams J, Sargin E (2016) Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM conference on recommender systems. ACM, New York
Di Noia T, Ostuni VC (2015) Recommender systems and linked open data. In: Reasoning web. Web logic rules: 11th international summer school 2015. Springer International Publishing, pp 88–113
Dong R, O’Mahony MP, Schaal M, McCarthy K, Smyth B (2016) Combining similarity and sentiment in opinion mining for product recommendation. J Intell Inf Syst 46(2):285–312
Durao F, Dolog P (2010) Extending a hybrid tag-based recommender system with personalization. In: SAC’10, Sierre, pp 1723–1727
Firan CS, Nejdl W, Paiu R (2007) The benefit of using tag-based profiles. In: LA-WEB’07, Santiago de Chile, pp 32–41
Gabrilovich E, Markovitch S (2009) Wikipedia-based semantic interpretation for natural language processing. J Artif Intell Res (JAIR) 34:443–498
Gedikli F, Jannach D (2013) Improving recommendation accuracy based on item-specific tag preferences. ACM Trans Intell Syst Technol 4(1):1–19
Gedikli F, Ge M, Jannach D (2011) Understanding recommendations by reading the clouds. In: EC-Web’11, Toulouse, pp 196–208
Gedikli F, Jannach D, Ge M (2014) How should I explain? A comparison of different explanation types for recommender systems. Int J Hum-Comput Stud 72(4):367–382. Elsevier BV, 2014
de Gemmis M, Lops P, Semeraro G, Basile P (2008) Integrating tags in a semantic content-based recommender. In: RecSys’08, Lausanne, pp 163–170
de Gemmis M, Lops P, Musto C, Narducci F, Semeraro G (2015) Semantics-aware content-based recommender systems. In: Ricci et al. (ed) Recommender systems handbook, 2nd edn. Springer US, New York, NY, USA, pp 119–159
Golder SA, Huberman BA (2006) Usage patterns of collaborative tagging systems. J Inf Sci 32(2):198–208
Hotho A, Jäschke R, Schmitz C, Stumme G (2006) Information retrieval in folksonomies: search and ranking. In: ESWC’06, Budva, pp 411–426
Jannach D, Adomavicius G (2016) Recommendations with a purpose. In: Proceedings of the 10th ACM conference on Recommender Systems (RecSys 2016), Boston, pp 7–10
Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems – an introduction. Cambridge University Press, Leiden
Ji A-T, Yeon C, Kim H-N, Jo G-S (2007) Collaborative tagging in recommender systems. In: AUS-AI’07, Gold Coast, pp 377–386
Katz G, Ofek N, Shapira B, Rokach L, Shani G (2011) Using Wikipedia to boost collaborative filtering techniques. In: Proceedings of the fifth ACM conference on Recommender Systems (RecSys ’11). ACM, New York, pp 285–288
Kubatz M, Gedikli F, Jannach D (2011) LocalRank – neighborhood-based, fast computation of tag recommendations. In: EC-Web’11, Toulouse, pp 258–269
Li X, Guo L, Zhao YE (2008) Tag-based social interest discovery. In: WWW’08, Beijing, pp 675–684
Liang H, Xu Y, Li Y (2012) Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems. In: ICDM’10, Sydney
Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80
Noll MG, Meinel C (2007) Web search personalization via social bookmarking and tagging. In: ISWC’07/ASWC’07, Busan, pp 367–380
van den Oord A, Dieleman, S., Schrauwen, B. (2013) Deep content-based music recommendation, Proceedings advances in Neural Information Processing Systems (NIPS), pp 2643–2651
Ostuni VC, Di Noia T, Di Sciascio E, Mirizzi R (2013) Top-N recommendations from implicit feedback leveraging linked open data. In: Proceedings of the 7th ACM conference on Recommender systems (RecSys ’13). ACM, New York, pp 85–92
Pitkow J, Schütze H, Cass T, Cooley R, Turnbull D, Edmonds A, Adar E, Breuel T (2002) Personalized search. Commun ACM 45(9):50–55
Rendle S, Schmidt-Thieme L (2010) Pairwise interaction tensor factorization for personalized tag recommendation. In: WSDM’10, New York, pp 81–90
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 727–736
Sen S, Vig J, Riedl JT (2009) Tagommenders: connecting users to items through tags. In: WWW’09, Madrid, pp 671–680
Seth A, Zhang J (2008) A social network based approach to personalized recommendation of participatory media content. In: ICWSM’08, Seattle
Shepitsen A, Gemmell J, Mobasher B, Burke R (2008) Personalized recommendation in social tagging systems using hierarchical clustering. In: RecSys’08, Lausanne, pp 259–266
Symeonidis P, Nanopoulos A, Manolopoulos Y (2008) Tag recommendations based on tensor dimensionality reduction. In: RecSys’08, Lausanne, pp 43–50
Tso-Sutter KHL, Marinho LB, Schmidt-Thieme L (2008) Tag-aware recommender systems by fusion of collaborative filtering algorithms. In: SAC’08, Fortaleza, pp 1995–1999
Vatturi PK, Geyer W, Dugan C, Muller M, Brownholtz B (2008) Tag-based filtering for personalized bookmark recommendations. In: CIKM’08, Napa Valley, pp 1395–1396
Vig J, Sen S, Riedl JT (2009) Tagsplanations: explaining recommendations using tags. In: IUI’09, Sanibel Island, pp 47–56
Vig J, Soukup M, Sen S, Riedl JT (2010) Tag expression: tagging with feeling. In: UIST’10, New York, pp 323–332
Wang H, Wang N, Yeung D-Y (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1235–1244
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 1267–1272
Xu G, Gu Y, Zhang Y, Yang Z, Kitsuregawa M (2011b) Toast: a topic-oriented tag-based recommender system. In: WISE’11, Sydney, pp 158–171
Zanardi V, Capra L (2011) A scalable tag-based recommender system for new users of the Social Web. In: DEXA’11, Toulouse, pp 542–557
Zhen Y, Li W-J, Yeung D-Y (2009) Tagicofi: tag informed collaborative filtering. In: RecSys’09, New York, pp 69–76
Recommended Reading
Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems – an introduction. Cambridge University Press, Leiden
Ricci F, Rokach L, Shapira B, Kantor PB (eds) (2011) Recommender systems handbook. Springer, New York
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
Gedikli, F., Jannach, D. (2018). Recommender Systems, Semantic-Based. 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_116
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7131-2_116
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