Abstract
Several existing recommender algorithms combine collaborative filtering and social/trust networks together in order to overcome the problems caused by data scarcity and to produce more effective recommendations for users. In general, those methods fuse a user’s own taste and his trusted friends/users’ tastes using an ensemble model where a parameter is used to balance these two components. However, this parameter is often set as a constant and with no regard to users’ individual characteristics. Aiming at introducing personalization to solve the above problem, we propose a local topology-based ensemble model to adaptively combine a user’s own taste and his trusted friends/users’ tastes. We take users’ clustering coefficients in the social/trust networks as the indicator to measure the consistence of their selecting trusted friends/users and leverage this local topology-based parameter in the ensemble model. To predict the likelihood of users’ purchasing actions on items, we also combine item ratings and sentiment values which are reflected in the review contents as the input to the adaptive ensemble model. We conduct comprehensive experiments which demonstrate the superiority of our adaptive algorithms over the existing ones.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Baluja S, Seth R, Sivakumar D, Jing Y, Yagnik J, Kumar S, Ravichandran D, Aly M (2008) Video suggestion and discovery for youtube: taking random walks through the view graph. In: Proceedings of the 17th international conference on World Wide Web, pp 895–904
Bao S, Xu S, Zhang L, Yan R, Su Z, Han D, Yu Y (2012) Mining social emotions from affective text. IEEE Trans Knowl Data Eng 2012:1658–1670
Bhatt R, Chaoji V, Parekh R (2010) Predicting product adoption in large-scale social networks. In: Proceedings of the 19th ACM international conference on information and knowledge management, pp 1039–1048
Cui P, Wang F, Liu S, Ou M, Yang S, Sun L (2011) Who should share what? item-level social influence prediction for users and posts ranking. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, pp 185–194
Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst, pp 143–177
George EI (1992) Explaining the gibbs sampler. Am Stat 46(3):167–174
Global (2009) Global advertising consumers trust real friends and virtual strangers the most. http://www.nielsen.com/us/en/newswire/2009/global-advertising-consumers-trust-real-friends-and-virtual-strangers-the-most.html
Golbeck JA (2005) Computing and applying trust in web-based social networks. Ph.D. thesis, College Park, MD, USA, aAI3178583
Golbeck J, Parsia B, Hendler J (2003) Trust networks on the semantic web. Proc Coop Intell Agents 2003:238–249
Jamali M, Ester M (2009) Using a trust network to improve top-n recommendation. Proc Third ACM Conf Recomm Syst 2009:181–188
Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. Proc Fourth ACM Conf Recomm Syst 2010:135–142
Jeh G, Widom J (2003) Scaling personalized web search. In: Proceedings of the 12th international conference on World Wide Web, pp 271–279
Konstas I, Stathopoulos V, Jose JM (2009) On social networks and collaborative recommendation. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval, pp 195–202
Kumar R, Novak J, Tomkins A (2006) Structure and evolution of online social networks. In: Proceedings of the 12th ACM SIGKDD International conference on knowledge discovery and data mining, pp 611–617
Lin C, He Y (2009) Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM conference on information and knowledge management, pp 375–384
Lu Y, Tsaparas P, Ntoulas A, Polanyi L (2010) Exploiting social context for review quality prediction. In: Proceedings of the 19th international conference on World Wide Web, pp 691–700
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, pp 203–210
Massa P, Avesani P (2007) Trust-aware recommender systems. In: Proceedings of the 2007 ACM conference on recommender systems, pp 17–24
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, pp 287–296
Moghaddam S, Ester M (2011) Ilda: Interdependent lda model for learning latent aspects and their ratings from online product reviews. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, pp 665–674
Page L, Brin S, Motwani R, Winograd T (1998) The pagerank citation ranking: bringing order to the web. Stanford
Rao Y, Li Q, Mao X, Wenyin L (2014) Sentiment topic models for social emotion mining. Inf Sci 266: 90–100
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, pp 175–186
Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th international conference on machine learning, pp 880–887
Sauro J (2013) Measuring user confidence in usability tests. http://www.measuringusability.com/blog/measuring-confidence.php
Seung-Taek P, Pennoc D, Madani O, Good N, DeCoste D (2006) Naive filterbots for robust cold-start recommendations. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 699–705
Shardanand U, Maes P (1995) Social information filtering: Algorithms for automating ‘word of mouth’. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 210–217
Tang J, Gao H, Liu H (2012) mTrust: discerning multi-faceted trust in a connected world. In: Proceedings of the fifth ACM international conference on web search and data mining, pp 93–102
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, pp 981–990
Zhang YC, Séaghdha DO, Quercia D, Jambor T (2012) Auralist: Introducing serendipity into music recommendation. In: Proceedings of the fifth ACM international conference on web search and data mining, pp 13–22
Zhou K, Yang SH, Zha H (2011) Functional matrx factorizations for cold-start recommendation. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, pp 315–324
Zou H, Gong Z, Zhang N, Zhao W, Guo J (2013) Trust-rank: a cold-start tolerant recommender system. Enterprise Information Systems, pp 1–22. doi:10.1080/17517575.2013.804587
Acknowledgments
Haitao Zou and Zhiguo Gong were supported in part by Fund of Science and Technology Development of Macau Government under FDCT/106/2012/A3 and FDCT/116/2013/A3 and in part by University Macau Research Committee under MYRG188-FST11-GZG and MYRG105-FST13-GZG. Nan Zhang was supported in part by the National Science Foundation under Grants 0852674, 0915834, 1117297, and 1343976. Any opinions, findings, conclusions, and/or recommendations expressed in this material, either expressed or implied, are those of the authors and do not necessarily reflect the views of the sponsors listed above.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zou, H., Gong, Z., Zhang, N. et al. Adaptive ensemble with trust networks and collaborative recommendations. Knowl Inf Syst 44, 663–688 (2015). https://doi.org/10.1007/s10115-014-0782-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-014-0782-7