{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:13:03Z","timestamp":1784178783681,"version":"3.55.0"},"reference-count":29,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2014,6,12]],"date-time":"2014-06-12T00:00:00Z","timestamp":1402531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,6,12]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>\u2013 The purpose of this paper is to propose an approach to generate recommendations for groups on the basis of social factors extracted from a social network. Group recommendation techniques traditionally assumed users were independent individuals, ignoring the effects of social interaction and relationships among users. In this work the authors analyse the social factors available in social networks in the light of sociological theories which endorse individuals\u2019 susceptibility to influence within a group.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>\u2013 The approach proposed is based on the creation of a group model in two stages: identifying the items that are representative of the majority's preferences, and analysing members\u2019 similarity; and extracting potential influence from members\u2019 interactions in a social network to predict a group's opinion on each item.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>\u2013 The promising results obtained when evaluating the approach in the movie domain suggest that individual opinions tend to be accommodated to group satisfaction, as demonstrated by the incidence of the aforementioned factors in collective behaviour, as endorsed by sociological research. Moreover the findings suggest that these factors have dissimilar impacts on group satisfaction.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>\u2013 The results obtained provide clues about how social influence exerted within groups could alter individuals\u2019 opinions when a group has a common goal. There is limited research in this area exploring social influence in group recommendations; thus the originality of this perspective lies in the use of sociological theory to explain social influence in groups of users, and the flexibility of the approach to be applied in any domain. The findings could be helpful for group recommender systems developers both at research and commercial levels.<\/jats:p><\/jats:sec>","DOI":"10.1108\/oir-08-2013-0187","type":"journal-article","created":{"date-parts":[[2014,7,10]],"date-time":"2014-07-10T12:29:43Z","timestamp":1404995383000},"page":"524-542","source":"Crossref","is-referenced-by-count":57,"title":["Social influence in group recommender systems"],"prefix":"10.1108","volume":"38","author":[{"given":"Ingrid","family":"Alina Christensen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvia","family":"Schiaffino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","reference":[{"key":"key2020123104472479300_b1","doi-asserted-by":"crossref","unstructured":"Ardissono, L. , Goy, A. , Petrone, G. , Segnan, G. and Torasso, G. (2003), \u201cIntrigue: personalized recommendation of tourist attractions for desktop and handset devices\u201d, Applied Artificial Intelligence, Vol. 17 No. 8, pp. 687-714.","DOI":"10.1080\/713827254"},{"key":"key2020123104472479300_b2","doi-asserted-by":"crossref","unstructured":"Bonhard, P. , Harries, C. , McCarthy, J. and Sasse, M.A. (2006), \u201cAccounting for taste: using profile similarity to improve recommender systems\u201d, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, New York, NY, pp. 1057-1066.","DOI":"10.1145\/1124772.1124930"},{"key":"key2020123104472479300_b3","unstructured":"Boratto, L. and Carta, S. (2011), \u201cState-of-the-art in group recommendation and new approaches for automatic identification of groups\u201d, in Soro, A. , Vargiu, E. , Armano, G. and Paddeu, G. (Eds), Information Retrieval and Mining in Distributed Environments (Studies in Computational Intelligence), Vol. 324. Springer, Berlin, pp. 1-20."},{"key":"key2020123104472479300_b4","doi-asserted-by":"crossref","unstructured":"Burke, R. (2002), \u201cHybrid recommender systems: survey and experiments\u201d, User Modeling and User-Adapted Interaction, Vol. 12 No. 4, pp. 331-370.","DOI":"10.1023\/A:1021240730564"},{"key":"key2020123104472479300_b5","doi-asserted-by":"crossref","unstructured":"Cantador, I. and Castells, P. (2012), Group Recommender Systems: New Perspectives in the Social Web, Intelligent Systems Reference Library, Vol. 32. Springer, Berlin.","DOI":"10.1007\/978-3-642-25694-3_7"},{"key":"key2020123104472479300_b6","doi-asserted-by":"crossref","unstructured":"Christensen, I. and Schiaffino, S. (2011), \u201cEntertainment recommender systems for group of users\u201d, Expert Systems with Applications, Vol. 38 No. 11, pp. 14127-14135.","DOI":"10.1016\/j.eswa.2011.04.221"},{"key":"key2020123104472479300_b7","unstructured":"Christensen, I. and Schiaffino, S. (2012), \u201cRatings estimation on group recommender systems\u201d, Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, Vol. 15 No. 50, pp. 18-29."},{"key":"key2020123104472479300_b8","doi-asserted-by":"crossref","unstructured":"Crandall, D. , Cosley, D. , Huttenlocher, D. , Kleinberg, J. and Suri, S. (2008), \u201cFeedback effects between similarity and social influence in online communities\u201d, Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, pp. 160-168.","DOI":"10.1145\/1401890.1401914"},{"key":"key2020123104472479300_b9","unstructured":"Friedkin, N.E. (2006), A Structural Theory of Social Influence, Cambridge University Press, New York, NY."},{"key":"key2020123104472479300_b10","doi-asserted-by":"crossref","unstructured":"Friedkin, N.E. and Johnsen, E.C. (2011), Social Influence Network Theory: A Sociological Examination of Small Group Dynamics, Cambridge University Press, New York, NY.","DOI":"10.1017\/CBO9780511976735"},{"key":"key2020123104472479300_b11","doi-asserted-by":"crossref","unstructured":"Gartrell, M. , Xing, X. , Lv, Q. , Beach, A. , Han, R. , Mishra, S. and Seada, K. (2010), \u201cEnhancing group recommendation by incorporating social relationship interactions\u201d, Proceedings of the 16th ACM International Conference on Supporting Group Work, ACM, New York, NY, pp. 97-106.","DOI":"10.1145\/1880071.1880087"},{"key":"key2020123104472479300_b12","doi-asserted-by":"crossref","unstructured":"He, J. and Chu, W.W. (2010), \u201cA social network-based recommender system (SNRS)\u201d, in Memon, N. , Xu, J.J. , Hicks, D.L. and Chen, H. (Eds), Data Mining for Social Network Data (Annals of Information Systems), Vol. 32. Springer, New York, NY, pp. 47-74.","DOI":"10.1007\/978-1-4419-6287-4_4"},{"key":"key2020123104472479300_b13","doi-asserted-by":"crossref","unstructured":"Jameson, A. and Smyth, B. (2007), \u201cRecommendations to groups\u201d, in Brusilovsky, P. , Kobsa, A. and Nejdl, W. (Eds), The Adaptive Web (Lecture Notes in Computer Science), Vol. 4321. Springer, Berlin, pp. 596-627.","DOI":"10.1007\/978-3-540-72079-9_20"},{"key":"key2020123104472479300_b14","doi-asserted-by":"crossref","unstructured":"Linden, G. , Smith, B. and York, J. (2003), \u201c Amazon.com recommendations: item-to-item collaborative filtering\u201d, Internet Computing, Vol. 7 No. 1, pp. 76-80.","DOI":"10.1109\/MIC.2003.1167344"},{"key":"key2020123104472479300_b15","doi-asserted-by":"crossref","unstructured":"Ma, H. , Yang, H. , Lyu, M.R. and King, I. (2008), \u201cSorec: social recommendation using probabilistic matrix factorization\u201d, Proceedings of the 17th ACM Conference on Information and Knowledge Management, ACM, New York, NY, pp. 931-940.","DOI":"10.1145\/1458082.1458205"},{"key":"key2020123104472479300_b16","unstructured":"Ma, H. , Zhou, T.C. , Lyu, M.R. and King, I. (2011), \u201cImproving recommender systems by incorporating social contextual information\u201d, ACM Transactions on Information Systems, Vol. 29 No. 2, pp. 9:1-9:23."},{"key":"key2020123104472479300_b17","doi-asserted-by":"crossref","unstructured":"Massa, P. and Avesani, P. (2004), \u201cTrust-aware collaborative filtering for recommender systems\u201d, in On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE, Springer, Berlin and Heidelberg, pp. 492-508.","DOI":"10.1007\/978-3-540-30468-5_31"},{"key":"key2020123104472479300_b18","doi-asserted-by":"crossref","unstructured":"Ortega, F. , Bobadilla, J. , Hernando, A. and Guti\u00e9rrez, A. (2013), \u201cIncorporating group recommendations to recommender systems: alternatives and performance\u201d, Information Processing and Management, Vol. 49 No. 4, pp. 895-901.","DOI":"10.1016\/j.ipm.2013.02.003"},{"key":"key2020123104472479300_b19","doi-asserted-by":"crossref","unstructured":"Pazzani, M.J. and Billsus, D. (2007), \u201cContent-based recommendation systems\u201d, in Brusilovsky, P. , Kobsa, A. and Nejdl, W. (Eds), The Adaptive Web, Springer, Berlin, pp. 325-341.","DOI":"10.1007\/978-3-540-72079-9_10"},{"key":"key2020123104472479300_b20","unstructured":"Pitsilis, G. and Knapskog, S.J. (2012), \u201cSocial trust as a solution to address sparsity-inherent problems of recommender systems\u201d, ACM RecSys 2009 Workshop on Recommender Systems and the Social Web, New York, NY, October, available at: http:\/\/arxiv.org\/ftp\/arxiv\/papers\/1208\/1208.1004.pdf (accessed 20 February 2014)."},{"key":"key2020123104472479300_b21","unstructured":"Quijano-Sanchez, L. , Recio-Garcia, J. , Diaz-Agudo, B. and Jimenez-Diaz, G. (2013), \u201cSocial factors in group recommender systems\u201d, ACM Transactions on Intelligent Systems and Technology, Vol. 4 No. 1, pp. 8:1-8:30."},{"key":"key2020123104472479300_b22","doi-asserted-by":"crossref","unstructured":"Schafer, J.B. , Frankowski, D. , Herlocker, J. and Sen, S. (2007), \u201cCollaborative filtering recommender systems\u201d, in Brusilovsky, P. , Kobsa, A. and Nejdl, W. (Eds), The Adaptive Web, Springer, Berlin, pp. 291-324.","DOI":"10.1007\/978-3-540-72079-9_9"},{"key":"key2020123104472479300_b23","unstructured":"Snijders, T.A. , Steglich, C.E. and Schweinberger, M. (2007), \u201cModeling the co-evolution of networks and behavior\u201d, Longitudinal Models in the Behavioral and Related Sciences, Lawrence Erlbaum, Mahwah, NJ, pp. 41-71."},{"key":"key2020123104472479300_b24","doi-asserted-by":"crossref","unstructured":"Subramani, M.R. and Rajagopalan, B. (2003), \u201cKnowledge-sharing and influence in online social networks via viral marketing\u201d, Communications of the ACM, Vol. 46 No. 12, pp. 300-307.","DOI":"10.1145\/953460.953514"},{"key":"key2020123104472479300_b25","doi-asserted-by":"crossref","unstructured":"Yang, J. and Leskovec, J. (2010), \u201cModeling information diffusion in implicit networks\u201d, 2010 IEEE 10th International Conference on Data Mining (ICDM), IEEE, New York, NY, pp. 599-608.","DOI":"10.1109\/ICDM.2010.22"},{"key":"key2020123104472479300_b26","doi-asserted-by":"crossref","unstructured":"Ye, M. , Liu, X. and Lee, W.C. (2012), \u201cExploring social influence for recommendation: a generative model approach\u201d, Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 671-680.","DOI":"10.1145\/2348283.2348373"},{"key":"key2020123104472479300_b27","unstructured":"Young, K. and Srivastava, J. (2007), \u201cImpact of social influence in e-commerce decision making\u201d, Proceedings of the 9th International Conference on Electronic Commerce, ACM, New York, NY, pp. 293-302."},{"key":"key2020123104472479300_b28","doi-asserted-by":"crossref","unstructured":"Yu, Z. , Zhou, X. , Hao, Y. and Gu, J. (2006), \u201cTV program recommendation for multiple viewers based on user profile merging\u201d, User Modeling and User-Adapted Interaction, Vol. 16 No. 1, pp. 63-82.","DOI":"10.1007\/s11257-006-9005-6"},{"key":"key2020123104472479300_b29","doi-asserted-by":"crossref","unstructured":"Zhou, Y. , Cheng, H. and Yu, J.X. (2009), \u201cGraph clustering based on structural\/attribute similarities\u201d, VLDB Endowment, Vol. 2 No. 1, pp. 718-729.","DOI":"10.14778\/1687627.1687709"}],"container-title":["Online Information Review"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/OIR-08-2013-0187","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/OIR-08-2013-0187\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/OIR-08-2013-0187\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:43:31Z","timestamp":1753397011000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/oir\/article\/38\/4\/524-542\/315104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,6,12]]},"references-count":29,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2014,6,12]]}},"alternative-id":["10.1108\/OIR-08-2013-0187"],"URL":"https:\/\/doi.org\/10.1108\/oir-08-2013-0187","relation":{},"ISSN":["1468-4527"],"issn-type":[{"value":"1468-4527","type":"print"}],"subject":[],"published":{"date-parts":[[2014,6,12]]}}}