Skip to main content
SpringerLink
Log in

Social recommendation: a review

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Recommender systems play an important role in helping online users find relevant information by suggesting information of potential interest to them. Due to the potential value of social relations in recommender systems, social recommendation has attracted increasing attention in recent years. In this paper, we present a review of existing recommender systems and discuss some research directions. We begin by giving formal definitions of social recommendation and discuss the unique property of social recommendation and its implications compared with those of traditional recommender systems. Then, we classify existing social recommender systems into memory-based social recommender systems and model-based social recommender systems, according to the basic models adopted to build the systems, and review representative systems for each category. We also present some key findings from both positive and negative experiences in building social recommender systems, and research directions to improve social recommendation capabilities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. http://www.amazon.com/s/ref=nb_sb_noss_1?url=search-alias%3Daps&field-keywords=computer

  2. http://www.amazon.com/

  3. http://www.netflix.com

  4. http://en.wikipedia.org/wiki/Netflix_Prize

  5. https://www.facebook.com/

  6. https://www.twitter.com/

  7. http://www.epinions.com

  8. http://www.linkedin.com/today/post/article/20121203134252-416648-the-power-of-social-recommendation

  9. http://twitaholic.com/

  10. http://www.firebellymarketing.com/2009/12/social-search-statistics-fromses-chicago.html

  11. http://en.wikipedia.org/wiki/Netflix_Prize

  12. http://www.grouplens.org/node/73

  13. http://www.trustlet.org/wiki/Downloaded_Epinions_dataset

  14. http://www.cs.ubc.ca/jamalim/datasets/

  15. http://www.public.asu.edu/jtang20/datasetcode/truststudy.htm

  16. See Foot note 15

References

  • Abbassi Z, Aperjis C, Huberman BA (2013) Friends versus the crowd: tradeoffs and dynamics. HP Report

  • Adali S (2013) Modeling trust context in networks. Springer, Berlin

  • Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

    Article  Google Scholar 

  • Agarwal N, Liu H, Tang L, Yu P (2008) Identifying the influential bloggers in a community. In: Proceedings of the international conference on Web search and web data mining. ACM, New York, pp 207–218

  • Agarwal V, Bharadwaj K. (2012) A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity. Social Network Analysis and Mining pp. 1–21

  • Au Yeung C, Iwata T (2011) Strength of social influence in trust networks in product review sites. In: Proceedings of the fourth ACM international conference on Web search and data mining. ACM, New York, pp 495–504

  • Baeza-Yates R, Ribeiro-Neto B, et al (1999) Modern information retrieval, vol 463. ACM press, New York

  • Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72

    Article  Google Scholar 

  • Belkin N.J., Croft W.B. (1992) Information filtering and information retrieval: two sides of the same coin? Commun ACM 35(12):29–38

    Article  Google Scholar 

  • Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence, pp 43–52

  • Chee SHS, Han J, Wang K (2001) Rectree: an efficient collaborative filtering method. In: Data warehousing and knowledge discovery. Springer, Berlin, pp 141–151

  • Chen WY, Chu JC, Luan J, Bai H, Wang Y, Chang EY (2009a) Collaborative filtering for orkut communities: discovery of user latent behavior. In: Proceedings of the 18th international conference on World wide web. ACM, New York, pp 681–690

  • Chen J, Geyer W, Dugan C, Muller M, Guy I (2009b) Make new friends, but keep the old: recommending people on social networking sites. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 201–210

  • Chen B, Guo J, Tseng B, Yang J (2011) User reputation in a comment rating environment. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 159–167

  • Cho J (2006) The mechanism of trust and distrust formation and their relational outcomes. J Retail 82(1):25–35

    Article  Google Scholar 

  • Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, pp 1082–1090

  • Chowdhury G (2010) Introduction to modern information retrieval. Facet publishing, London

  • Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M (1999) Combining content-based and collaborative filters in an online newspaper. In: Proceedings of ACM SIGIR workshop on recommender systems, vol 60. Citeseer, New York

  • Crandall D, Cosley D, Huttenlocher D, Kleinberg J, Suri S (2008) Feedback effects between similarity and social influence in online communities. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, pp 160–168

  • Davis D, Lichtenwalter R, Chawla NV (2013) Supervised methods for multi-relational link prediction. Soc Netw Anal Min, pp 1–15

  • Dellarocas C, Zhang XM, Awad NF (2007) Exploring the value of online product reviews in forecasting sales: the case of motion pictures. J Interact Mark 21(4):23–45

    Article  Google Scholar 

  • Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst 22(1):143–177

    Article  Google Scholar 

  • Ding Y, Li X (2005) Time weight collaborative filtering. In: Proceedings of the 14th ACM international conference on Information and knowledge management. ACM, New York, pp 485–492

  • Dunbar R (2010) How many friends does one person need? Faber & Faber

  • Dunlavy D, Kolda T, Acar E (2011) Temporal link prediction using matrix and tensor factorizations. ACM Trans Knowl Discov Data (TKDD) 5(2):10

    Google Scholar 

  • Ellenberg J (2008) This psychologist might outsmart the math brains competing for the netflix prize. Wired Mag, pp 114–122

  • Falcone R, Castelfranchi C (2010) Transitivity in trust a discussed property. Citeseer, New York

  • Fang Y, Si L (2011) Matrix co-factorization for recommendation with rich side information and implicit feedback. In: Proceedings of the 2nd international workshop on information heterogeneity and fusion in recommender systems. ACM, New York, pp 65–69

  • Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174

    Article  MathSciNet  Google Scholar 

  • Gao H, Tang J, Liu H (2012a) Exploring social-historical ties on location-based social networks. In: Proceedings of the 6th international AAAI conference on weblogs and social media

  • Gao H, Tang J, Liu H (2012b) gscorr: Modeling geo-social correlations for new check-ins on location-based social networks. In: Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, New York, pp 1582–1586

  • Gefen D, Karahanna E, Straub D (2003) Trust and tam in online shopping: an integrated model. Mis Q, pp 51–90

  • Golbeck J (2006a) Generating predictive movie recommendations from trust in social networks. Trust Manag, pp 93–104

  • Golbeck J (2006b) Generating predictive movie recommendations from trust in social networks. Springer, Berlin

  • Golbeck J (2009) Trust and nuanced profile similarity in online social networks. ACM Trans Web 3(4):1–33

    Article  Google Scholar 

  • Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70

    Article  Google Scholar 

  • Goldberg K, Roeder T, Gupta D, Perkins C (2001) Eigentaste: a constant time collaborative filtering algorithm. Inf Retr 4(2):133–151

    Article  MATH  Google Scholar 

  • Good N, Schafer JB, Konstan JA, Borchers A, Sarwar B, Herlocker J, Riedl J (1999) Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of the national conference on artificial intelligence, pp 439–446

  • Granovetter M (1973) The strength of weak ties. Am J Soc 78(6):1360–1380

    Article  Google Scholar 

  • Granovetter M (1983) The strength of weak ties: a network theory revisited. Soc Theory 1(1):201–233

    Article  Google Scholar 

  • Guy I, Carmel D (2011) Social recommender systems. In: Proceedings of the 20th international conference companion on World wide web. ACm, New York, pp 283–284

  • Guy I, Jacovi M, Shahar E, Meshulam N, Soroka V, Farrell S (2008) Harvesting with sonar: the value of aggregating social network information. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 1017–1026

  • Guy I, Jacovi M, Perer A, Ronen I, Uziel E (2010) Same places, same things, same people?: mining user similarity on social media. In: Proceedings of the 2010 ACM conference on computer supported cooperative work. ACM, New York, pp 41–50

  • Herlocker J, Konstan J, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 230–237

  • Hofmann T (2004) Latent semantic models for collaborative filtering. ACM Trans Inf Syst (TOIS) 22(1):89–115

    Article  Google Scholar 

  • Hong L, Doumith AS, Davison BD (2013) Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In: Proceedings of the sixth ACM international conference on Web search and data mining. ACM, New York, pp 557–566

  • Huang Z, Zeng D, Chen H (2004) A link analysis approach to recommendation under sparse data. In: Proceedings of 2004 Americas conference on information systems

  • IBM (2012) Ibm’s black friday report says twitter delivered 0 percent of referral traffic and facebook sent just 0.68 percent. In: https://strme.wordpress.com/2012/11/27/ibms-black-friday-report-says-twitter-delivered-0-percent-of-referral-traffic-and-facebook-sent-just-0-68-percent/

  • Jamali M, Ester M (2009) Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 397–406

  • Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the fourth ACM conference on recommender systems. ACM, New York, pp 135–142

  • Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction. Cambridge University Press, Cambridge

  • Jiang M, Cui P, Liu R, Yang Q, Wang F, Zhu W, Yang S.(2012) Social contextual recommendation. In: Proceedings of the 22th ACM international conference on Information and knowledge management. ACM, New York

  • Karypis G (2001) Evaluation of item-based top-n recommendation algorithms. In: Proceedings of the tenth international conference on Information and knowledge management. ACM, New York, pp 247–254

  • Kautz H, Selman B, Shah M (1997) Referral web: combining social networks and collaborative filtering. Commun ACM 40(3):63–65

    Article  Google Scholar 

  • King I, Lyu MR, Ma H (2010) Introduction to social recommendation. In: Proceedings of the 19th international conference on World wide web. ACM, New York, pp 1355–1356

  • Kleinberg J (1999) Authoritative sources in a hyperlinked environment. J ACM 46(5):604–632

    Article  MathSciNet  MATH  Google Scholar 

  • Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, pp 426–434

  • Koren Y (2009) Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, pp 447–456

  • Leskovec J, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web 1(1):5

    Article  Google Scholar 

  • Leskovec J, Huttenlocher D, Kleinberg J (2010) Predicting positive and negative links in online social networks. In: Proceedings of the 19th international conference on world wide web

  • Levin DZ, Cross R (2004) The strength of weak ties you can trust: the mediating role of trust in effective knowledge transfer. Manag Sci 50(11):1477–1490

    Article  Google Scholar 

  • Li Y, Hu J, Zhai C, Chen Y (2010) Improving one-class collaborative filtering by incorporating rich user information. In: Proceedings of the 19th ACM international conference on Information and knowledge management. ACM, New York, pp 959–968

  • Liu J, Zhang F, Song X, Song YI, Lin CY, Hon HW (2013) What’s in a name?: an unsupervised approach to link users across communities. In: Proceedings of the sixth ACM international conference on Web search and data mining. ACM, New York, pp 495–504

  • Lü L, Medo M, Yeung CH, Zhang YC, Zhang ZK, Zhou T (2012) Recommender systems. Phys Rep

  • Ma H, Yang H, Lyu M, King I (2008) Sorec: social recommendation using probabilistic matrix factorization. In: Proceeding of the 17th ACM conference on Information and knowledge management. ACM, New York, pp 931–940

  • Ma H, King I, Lyu MR (2009a) Learning to recommend with social trust ensemble. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 203–210

  • Ma H, Lyu MR, King I (2009b) Learning to recommend with trust and distrust relationships. In: Proceedings of the third ACM conference on recommender systems. ACM, New York, pp 189–196

  • Ma N, Lim E, Nguyen V, Sun A, Liu H (2009c) Trust relationship prediction using online product review data. In: Proceeding of the 1st ACM international workshop on complex networks meet information and knowledge management. ACM, New York, pp 47–54

  • Ma H, Zhou TC, Lyu MR, King I (2011a) Improving recommender systems by incorporating social contextual information. ACM Trans Inf Syst 29(2):9

    Article  MATH  Google Scholar 

  • Ma H, Zhou D, Liu C, Lyu M, King I (2011b) Recommender systems with social regularization. In: Proceedings of the fourth ACM international conference on Web search and data mining. ACM, New York, pp 287–296

  • Marsden P, Friedkin N (1993) Network studies of social influence. Soc Methods Res 22(1):127–151

    Article  Google Scholar 

  • Massa P (2007) A survey of trust use and modeling in real online systems. Trust E Serv Technol Prac Chall

  • Massa P, Avesani P (2004) Trust-aware collaborative filtering for recommender systems. In: On the move to meaningful internet systems 2004: CoopIS, DOA, and ODBASE. Springer, Berlin, pp 492–508

  • Massa P, Avesani P (2005) Controversial users demand local trust metrics: an experimental study on epinions. com community. In: Proceedings of the national conference on artificial intelligence, vol 20. p. 121. Menlo Park, CA; Cambridge, MA; London; ; MIT Press; 1999

  • Massa P, Avesani P (2007) Trust-aware recommender systems. In: Proceedings of the 2007 ACM conference on recommender systems. ACM, New York, pp 17–24

  • Matthew R, Pedro D (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining

  • McKnight D, Choudhury V, Kacmar C (2003) Developing and validating trust measures for e-commerce: an integrative typology. Inf Syst Res 13(3):334–359

    Article  Google Scholar 

  • McPherson M, Smith-Lovin L, Cook J (2001) Birds of a feather: homophily in social networks. Annu Rev Soc, pp 415–444

  • Mei T, Yang B, Hua XS, Yang L, Yang SQ, Li S (2007) Videoreach: an online video recommendation system. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 767–768

  • Menon A, Elkan C (2011) Link prediction via matrix factorization. Mach Learn Knowl Discov Databases, pp 437–452

  • Miyahara K, Pazzani MJ (2000) Collaborative filtering with the simple bayesian classifier. In: PRICAI 2000 topics in artificial intelligence. Springer, Berlin, pp 679–689

  • Mooney RJ, Bennett PN, Roy L (1998) Book recommending using text categorization with extracted information. In: Proc. Recommender Systems Papers from 1998 Workshop, Technical Report WS-98-08

  • Narayanan A, Shmatikov V (2009) De-anonymizing social networks. In: 2009 30th IEEE symposium on security and privacy. IEEE, New york, pp 173–187

  • Newman ME (2005) Power laws, pareto distributions and Zipf’s law. Contemp Phys 46(5):323–351

    Article  Google Scholar 

  • Noel J, Sanner S, Tran KN, Christen P, Xie L, Bonilla EV, Abbasnejad E, Della Penna N (2012) New objective functions for social collaborative filtering. In: Proceedings of the 21st international conference on World Wide Web. ACM, New York, pp 859–868

  • Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web. Stanford InfoLab

  • Pan R, Zhou Y, Cao B, Liu NN, Lukose R, Scholz M, Yang Q (2008) One-class collaborative filtering. In: Eighth IEEE international conference on data mining. IEEE, New York, pp 502–511

  • Paterek A. (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD cup and workshop, vol 2007, pp 5–8

  • Pazzani MJ (1999) A framework for collaborative, content-based and demographic filtering. Artif Intell Rev 13(5–6):393–408

    Article  Google Scholar 

  • Pazzani M., Billsus D. (1997) Learning and revising user profiles: the identification of interesting web sites. Mach Learn 27(3):313–331

    Article  Google Scholar 

  • Quora (2012) Why does the startup idea of social recommendations consistently fail? In: http://www.quora.com/Why-does-the-startup-idea-of-social-recommendations-consistently-fail

  • Raghavan S, Gunasekar S, Ghosh J (2012) Review quality aware collaborative filtering. In: Proceedings of the sixth ACM conference on recommender systems. ACM, New York, pp 123–130

  • Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on Computer supported cooperative work. ACM, New York, pp 175–186

  • Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In: Recommender systems handbook, pp 1–35

  • Salakhutdinov R, Mnih A (2008) Probabilistic matrix factorization. Adv Neural Inf Process Syst 20:1257–1264

    Google Scholar 

  • Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web. ACM, New York, pp 285–295

  • Scellato S, Mascolo C, Musolesi M, Latora V (2010) Distance matters: geo-social metrics for online social networks. In: Proceedings of WOSN 10

  • Schafer JB, Konstan JA, Riedl J (2001) E-commerce recommendation applications. Data Min Knowl Discov 5(1):115–153

    Article  MATH  Google Scholar 

  • Scott J (2011) Social network analysis: developments, advances, and prospects. Soc Netw Anal Min 1(1):21–26

    Article  Google Scholar 

  • Scott J (2012) Social network analysis. SAGE Publications Limited, London

  • Sigurbjörnsson B, Van Zwol R (2008) Flickr tag recommendation based on collective knowledge. In: Proceedings of the 17th international conference on world wide web. ACM, New York, pp 327–336

  • Sinha R, Swearingen K (2001) Comparing recommendations made by online systems and friends. In: Proceedings of the Delos-NSF workshop on personalization and recommender systems in digital libraries, vol. 106. Dublin, Ireland

  • Soboroff I, Nicholas C (1999) Combining content and collaboration in text filtering. In: Proceedings of international joint conference on artificial intelligence workshop: machine learning for information filtering

  • Su X, Khoshgoftaar T (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:4

  • Sun Y., Han J. (2012) Mining heterogeneous information networks: principles and methodologies. Synth Lect Data Min Knowl Discov 3(2):1–159

    Article  Google Scholar 

  • Symeonidis P, Tiakas E, Manolopoulos Y (2011) Product recommendation and rating prediction based on multi-modal social networks. In: Proceedings of the fifth ACM conference on recommender systems. ACM, New York, pp 61–68

  • Tang L, Liu H (2009) Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 817–826

  • Tang L, Liu H (2010) Community detection and mining in social media. Synth Lect Data Min Knowl Discov 2(1):1–137

    Article  Google Scholar 

  • Tang J, Gao H, Liu H (2012a) mTrust: Discerning multi-faceted trust in a connected world. In: Proceedings of the fifth ACM international conference on web search and data mining. ACM, New York, pp 93–102

  • Tang J, Gao H, Liu H, Das Sarma A (2012b) eTrust: Understanding trust evolution in an online world. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 253–261

  • Tang J, Gao H, Hu X, Liu H (2013a) Context-aware review helpfulness rating prediction. In: RecSys

  • Tang J, Gao H, Hu X, Liu H (2013b) Exploiting homophily effect for trust prediction. In: Proceedings of the sixth ACM international conference on Web search and data mining. ACM, New York, pp 53–62

  • Tang J, Hu X, Gao H, Liu H (2013c) Exploiting local and global social context for recommendation. In: IJCAI

  • Ungar LH, Foster DP (1998) .: Clustering methods for collaborative filtering. In: AAAI workshop on recommendation systems, vol 1

  • Vasuki V, Natarajan N, Lu Z, Dhillon IS (2010) Affiliation recommendation using auxiliary networks. In: Proceedings of the fourth ACM conference on recommender systems. ACM, New York, pp 103–110

  • Victor P, Cornelis C, De Cock M, Teredesai AM (2009) A comparative analysis of trust-enhanced recommenders for controversial items. In: Proceedings of the international AAI conference on weblogs and social media, pp 342–345

  • Victor P, De Cock M, Cornelis C (2011) Trust and recommendations. In: Recommender systems handbook. Springer, Berlin, pp 645–675

  • Wasserman S, Faust K (1994) Social network analysis: methods and applications, vol 8. Cambridge University Press, Cambridge

  • Weng J, Lim E, Jiang J, He Q (2010) Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the third ACM international conference on web search and data mining. ACM, New York, pp 261–270

  • Wu HC, Luk RWP, Wong KF, Kwok KL (2008) Interpreting tf-idf term weights as making relevance decisions. ACM Trans Inf Syst (TOIS) 26(3):13

    Article  Google Scholar 

  • Xiang R, Neville J, Rogati M (2010) Modeling relationship strength in online social networks. In: Proceedings of the 19th international conference on World wide web

  • Yang SH, Long B, Smola A, Sadagopan N, Zheng Z, Zha H (2011) Like like alike: joint friendship and interest propagation in social networks. In: Proceedings of the 20th international conference on World wide web. ACM, New York, pp 537–546

  • Yildirim H., Krishnamoorthy M.S. (2008) A random walk method for alleviating the sparsity problem in collaborative filtering. In: Proceedings of the 2008 ACM conference on recommender systems. ACM, New York, pp 131–138

  • Yuan Q, Zhao S, Chen L, Liu Y, Ding S, Zhang X, Zheng W (2009) Augmenting collaborative recommender by fusing explicit social relationships. In: Workshop on recommender systems and the social web, Recsys 2009

  • Yuan Q, Chen L, Zhao S (2011) Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation. In: Proceedings of the fifth ACM conference on recommender systems. ACM, New York, pp 245–252

  • Zafarani R, Liu H (2009) Connecting corresponding identities across communities. In: Proceedings of the 3rd international conference on weblogs and social media (ICWSM09)

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Arizona State University, Tempe, USA

    Jiliang Tang, Xia Hu & Huan Liu

Authors
  1. Jiliang Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xia Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiliang Tang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tang, J., Hu, X. & Liu, H. Social recommendation: a review. Soc. Netw. Anal. Min. 3, 1113–1133 (2013). https://doi.org/10.1007/s13278-013-0141-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13278-013-0141-9

Keywords

Search

Navigation