Glossary
- Collaborative filtering:
-
Given a ratings matrix R, representing the preferences of users U for items I, recommend to each user a list of items in descending order of their relevance for the user. The relevance scores are estimated based on ratings of similar users
- Ratings matrix:
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Assume a set of users U and a set of items I in the recommender system. A user u ∈ U might provide her preference for an item i ∈ I in form of a rating denoted by rating (u, i), which typically takes values in (Adomavicius et al. 2011; Blei et al. 2003). The preferences of users for individual items are represented by a ratings matrix R, where the Ru,i entry corresponds to rating (u, i)
- Recommendation:
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A suggestion or proposal to a user for an item, e.g., book, movie, video, news article, that is potentially interesting for the user
- Recommender system:
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A system or engine that produces recommendations by predicting the...
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References
Adomavicius G, Sankaranarayanan R, Sen S, Tuzhilin A (2005) Incorporating contextual information in recommender systems using a multidimensional approach. TIS, 23(1):103–145
Adomavicius G, Manouselis N, Kwon Y (2011) Multi-criteria recommender systems. In: Recommender systems handbook. Springer US, Boston, MA, pp 769–803
Amer-Yahia S, Lakshmanan LVS, Yu C (2009) Socialscope: enabling information discovery on social content sites. In: CIDR, Asilomar, CA, USA
Backstrom L, Sun E, Marlow C (2010) Find me if you can: improving geographical prediction with social and spatial proximity. In: Proceedings of the 19th international conference on world wide web, WWW 2010, Raleigh, 26–30 Apr 2010. pp 61–70
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(4–5):9931022
Chen G, Chen L (2014) Recommendation based on contextual opinions. In: UMAP, Aalborg, Denmark
Cheng Z, Caverlee J, Lee K (2010) You are where you tweet: a content-based approach to geo-locating twitter users. In: Proceedings of the 19th ACM conference on information and knowledge management, CIKM 2010, Toronto, 26–30 Oct 2010, pp 759–768
Christophides V, Efthymiou V, Stefanidis K (2015) Entity resolution in the web of data. Synthesis lectures on the semantic web: theory and technology. Morgan & Claypool Publishers, California, USA
Davis CA Jr, Pappa GL, de Oliveira DRR, de Lima Arcanjo F (2011) Inferring the location of twitter messages based on user relationships. Trans GIS 15(6):735–751
Desrosiers C, Karypis G (2011) A comprehensive survey of neighborhood-based recommendation methods. In: Recommender systems handbook. Springer US, Boston, MA, pp 107–144
Ding Y, Li X (2005) Time weight collaborative filtering. In: CIKM, ACM, New York, NY, USA
Fagin R (2002) Combining fuzzy information: an overview. SIGMOD Rec 31(2):109–118
Fu Y, Zhu X, Li B (2013) A survey on instance selection for active learning. Knowl Inf Syst 35(2):249–283
Han B, Cook P, Baldwin T (2012) Geolocation prediction in social media data by finding location indicative words. In: COLING 2012, 24th international conference on computational linguistics, proceedings of the conference: technical papers, 8–15 Dec 2012, Mumbai, pp 1045–1062
Hecht B, Hong L, Suh B, Chi EH (2011) Tweets from Justin Bieber’s heart: the dynamics of the location field in user profiles. In: Proceedings of the international conference on human factors in computing systems, CHI 2011, Vancouver, 7–12 May 2011, pp 237–246
Herrmann LR (1976) Laplacian-isoparametric grid generation scheme. J Eng Mech Div 5(102):749756
Jamali M, Ester M (2009) Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: SIGKDD, KDD. ACM, Paris, France, pp 397–406
Kondylakis H, Koumakis L, Kazantzaki E, Chatzimina M, Psaraki M, Marias K, Tsiknakis M (2015) Patient empowerment through personal medical recommendations. In: MEDINFO, Sao Paulo, Brazil
Konstan JA, Miller BN, Maltz D, Herlocker JL, Gordon LR, Riedl J (1997) Grouplens: applying collaborative filtering to use net news. Commun ACM 40(3):77–87
Koren Y (2009) Collaborative filtering with temporal dynamics. In: SIGKDD, ACM, New York, NY, USA
Li R, Wang S, Deng H, Wang R, Chang KC-C (2012) Towards social user profiling: unified and discriminative influence model for inferring home locations. In: The 18th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ‘12, Beijing, 12–16 Aug 2012, pp 1023–1031
Li R, Wang C, Chang KC (2014) User profiling in an ego network: co-profiling attributes and relationships. In: 23rd international world wide web conference, WWW ‘14, Seoul, 7–11 Apr 2014, pp 819–830
McAuley J, Leskovec J (2013a) Hidden factors and hidden topics: understanding rating dimensions with review text. In: RecSys, ACM, New York, NY, USA
McAuley JJ, Leskovec J (2013b) From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In: Proceedings of the 22nd international conference on world wide web. ACM, New York, NY, USA, pp 897–908
Mooney RJ, Roy L (2000) Content-based book recommending using learning for text categorization. In: ACM DL, ACM, New York, NY, USA
Pennacchiotti M, Gurumurthy S (2011) Investigating topic models for social media user recommendation. In: WWW, ACM, New York, NY, USA, pp 101–102
Prelic A, Bleuler S, Zimmermann P, Wille A, Bühlmann P, Gruissem W, Hennig L, Thiele L, Zitzler E (2006) A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9):1122–1129
Sadilek A, Kautz HA, Bigham JP (2012) Finding your friends and following them to where you are. In: Proceedings of the fifth international conference on web search and web data mining, WSDM 2012, Seattle, 8–12 Feb 2012, pp 723–732
Stefanidis K, Shabib N, Nørvåg K, Krogstie J (2012) Contextual recommendations for groups. In: Advances in conceptual modeling ER 2012 workshops, Springer Berlin Heidelberg, Berlin, Heidelberg, pp 89–97
Stefanidis K, Ntoutsi E, Petropoulos M, Nørvåg K, Kriegel H (2013) A framework for modeling, computing and presenting time-aware recommendations. Large Scale Data Know Centered Syst 10:146–172
Tang J, Yao L, Zhang D, Zhang J (2010) A combination approach to web user profiling. TKDD 5(1):2
Wischenbart M, Mitsch S, Kapsammer E, Kusel A, Pröll B, Retschitzegger W, Schwinger W, Schönböck J, Wimmer M, Lechner S (2012) User profile integration made easy: model-driven extraction and transformation of social network schemas. In: WWW, ACM, New York, NY, USA
Zhao WX, Jiang J, Weng J, He J, Lim E, Yan H, Li X (2011) Comparing twitter and traditional media using topic models. In: ECIR, Springer Berlin Heidelberg, Berlin, Heidelberg, pp 338–349
Zimmermann M, Ntoutsi E, Spiliopoulou M (2015) Discovering and monitoring product features and the opinions on them with OPINSTREAM. Neurocomputing 150:318–330
Recommended Reading
Shi Y, Larson M, Hanjalic A (2014) Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput Surv 47(1):3:1–3:45
Stefanidis K, Ntoutsi E, Kondylakis H Information hunting: the many faces of recommendations for data exploration. ACM SIGMOD Blog. http://wp.sigmod.org/?p=1580
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Stefanidis, K., Ntoutsi, E., Kondylakis, H., Velegrakis, Y. (2018). Social-Based Collaborative Filtering. 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_110171
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