Glossary
- Collaborative filtering:
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A type of recommendation technique
- Matrix factorization:
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Factorizing the user-item matrix into user latent matrix and item latent matrix
- Recommender system:
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A system that provides recommendations for users
- Social relations:
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Various social relationships between users, like social trust relationships
Definition
The research of social recommendation aims at modeling recommender systems more accurately and realistically. The characteristic of social recommendation that is different from the tradition recommender system is the availability of social network, i.e., relational information among the users. Social recommendation focuses on how to utilize user social information to effectively and efficiently compute recommendation results.
Introduction
As the exponential growth of information generated on the World Wide Web, the Information Filtering...
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Bedi P, Kaur H, Marwaha S (2007) Trust based recommender system for semantic web. In: Proceedings of IJCAI’07, Hyderabad, pp 2677–2682
Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of UAI’98, Madison
Canny J (2002) Collaborative filtering with privacy via factor analysis. In: Proceedings of SIGIR’02, Tampere, pp 238–245
Deshpande M, Karypis G (2004) Item-based top-n recommendation. ACM Trans Inf Syst 22(1):143–177
Hofmann T (2003) Collaborative filtering via Gaussian probabilistic latent semantic analysis. In: Proceedings of SIGIR’03, Toronto, pp 259–266
Hofmann T (2004) Latent semantic models for collaborative filtering. ACM Trans Inf Syst 22(1):89–115. https://doi.org/10.1145/963770.963774
Huang Z, Chen H, Zeng D (2004) Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans Inf Syst 22(1):116–142
Jin R, Chai JY, Si L (2004) An automatic weighting scheme for collaborative filtering. In: Proceedings of SIGIR’04, Sheffield, pp 337–344
Kohrs A, Merialdo B (1999) Clustering for collaborative filtering applications. In: Proceedings of CIMCA, Gold Coast
Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Intern Comput 7:76–80
Liu NN, Yang Q (2008) Eigenrank: a ranking-oriented approach to collaborative filtering. In: Proceedings of SIGIR’08, Singapore, pp 83–90
Ma H, King I, Lyu MR (2007) Effective missing data prediction for collaborative filtering. In: Proceedings of SIGIR’07, Amsterdam, pp 39–46
Ma H, King I, Lyu MR (2009) Learning to recommend with social trust ensemble. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval, SIGIR’09, Boston, pp 203–210
Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the fourth ACM international conference on web search and data mining, WSDM’11, Hong Kong, pp 287–296
Massa P, Avesani P (2004) Trust-aware collaborative filtering for recommender systems. In: Proceedings of CoopIS/DOA/ODBASE, Irvine, pp 492–508
Massa P, Avesani P (2007) Trust-aware recommender systems. In: Proceedings of RecSys’07, Minneapolis, pp 17–24
O’Donovan J, Smyth B (2005) Trust in recommender systems. In: Proceedings of IUI’05, San Diego, pp 167–174
Rennie JDM, Srebro N (2005) Fast maximum margin matrix factorization for collaborative prediction. In: Proceedings of ICML’05, Bonn
Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of CSCW’94, Chapel Hill
Salakhutdinov R, Mnih A (2008a) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of ICML’08, Helsinki
Salakhutdinov R, Mnih A (2008b) Probabilistic matrix factorization. In: Proceedings of NIPS’08, vol 20, Vancouver
Sarwar B, Karypis G, Konstan J, Reidl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of WWW’01, Hong Kong, pp 285–295
Si L, Jin R (2003) Flexible mixture model for collaborative filtering. In: Proceedings of ICML’03, Washington, DC
Sinha RR, Swearingen K (2001) Comparing recommendations made by online systems and friends. In: DELOS workshop: personalisation and recommender systems in digital libraries, Dublin
Srebro N, Jaakkola T (2003) Weighted low-rank approximations. In: Proceedings of ICML’03, Washington, DC, pp 720–727
Srebro N, Rennie JDM, Jaakkola T (2004) Maximum-margin matrix factorization. In: Proceedings of NIPS’04, Vancouver
Zhang Y, Koren J (2007) Efficient Bayesian hierarchical user modeling for recommendation system. In: Proceedings of SIGIR’07, Amsterdam, pp 47–54
Acknowledgments
The work described in this article is supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project No.: CUHK 413212 and CUHK 415212).
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Ma, H., King, I., Lyu, M.R. (2018). Social Recommendation in Dynamic Networks. 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_189
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DOI: https://doi.org/10.1007/978-1-4939-7131-2_189
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