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Social-Based Collaborative Filtering

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Encyclopedia of Social Network Analysis and Mining

Synonyms

Collaborative filtering using social data; Social-Based Recommendations

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:

Assume a set of users U and a set of items I in the recommender system. A user uU might provide her preference for an item iI 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:

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:

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

    Google Scholar 

  • Adomavicius G, Manouselis N, Kwon Y (2011) Multi-criteria recommender systems. In: Recommender systems handbook. Springer US, Boston, MA, pp 769–803

    Google Scholar 

  • Amer-Yahia S, Lakshmanan LVS, Yu C (2009) Socialscope: enabling information discovery on social content sites. In: CIDR, Asilomar, CA, USA

    Google Scholar 

  • 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

    Google Scholar 

  • Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(4–5):9931022

    MATH  Google Scholar 

  • Chen G, Chen L (2014) Recommendation based on contextual opinions. In: UMAP, Aalborg, Denmark

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Desrosiers C, Karypis G (2011) A comprehensive survey of neighborhood-based recommendation methods. In: Recommender systems handbook. Springer US, Boston, MA, pp 107–144

    Google Scholar 

  • Ding Y, Li X (2005) Time weight collaborative filtering. In: CIKM, ACM, New York, NY, USA

    Google Scholar 

  • Fagin R (2002) Combining fuzzy information: an overview. SIGMOD Rec 31(2):109–118

    Article  Google Scholar 

  • Fu Y, Zhu X, Li B (2013) A survey on instance selection for active learning. Knowl Inf Syst 35(2):249–283

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Herrmann LR (1976) Laplacian-isoparametric grid generation scheme. J Eng Mech Div 5(102):749756

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Koren Y (2009) Collaborative filtering with temporal dynamics. In: SIGKDD, ACM, New York, NY, USA

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • McAuley J, Leskovec J (2013a) Hidden factors and hidden topics: understanding rating dimensions with review text. In: RecSys, ACM, New York, NY, USA

    Google Scholar 

  • 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

    Google Scholar 

  • Mooney RJ, Roy L (2000) Content-based book recommending using learning for text categorization. In: ACM DL, ACM, New York, NY, USA

    Google Scholar 

  • Pennacchiotti M, Gurumurthy S (2011) Investigating topic models for social media user recommendation. In: WWW, ACM, New York, NY, USA, pp 101–102

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Tang J, Yao L, Zhang D, Zhang J (2010) A combination approach to web user profiling. TKDD 5(1):2

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Zimmermann M, Ntoutsi E, Spiliopoulou M (2015) Discovering and monitoring product features and the opinions on them with OPINSTREAM. Neurocomputing 150:318–330

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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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Correspondence to Kostas Stefanidis .

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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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