Abstract
Collaborative Filtering (CF) is the most popular recommendation technique that uses preferences of users in a community to make personal recommendations for other users. Despite its popularity and success, CF suffers from the data sparsity and cold-start problems. To alleviate these issues, in recent years, there has been an upsurge of interest in exploiting trust information to improve the performance of CF. In general, trust has a number of distinct properties such as asymmetry, transitivity, dynamicity and context-dependency. However, conventional trust-based CF systems do not address trust computation by considering all the properties of trust. Particularly, the context-dependency property has received less attention in the existing approaches. The consideration of all these properties leads to more accurate recommendations since the quality of the inferred is improved. In this paper, we propose a novel trust-based approach, called Semantic-enhanced Trust based Ant Recommender System (STARS), which satisfies all the properties mentioned above. Using ant colony optimization, the proposed system performs a depth- first search for the optimal trust paths in the trust network and selects the best neighbors of an active user to provide better recommendations. To consider the context-dependency property, trust inference in STARS depends on the semantic descriptions of items. Incorporation of both global and local trust in CF-based recommender systems in addition to the trust computation based on the semantic features of items allows STARS to alleviate the data sparsity, cold-start and “multiple-interests and multiple-content” problems of CF. Experimental results on real-world data sets show that STARS outperforms its counterparts in terms of prediction accuracy and recommendation quality and can overcome the above problems.
Similar content being viewed by others
Notes
The formula can be referred to as the percentage change defined in http://www.math.umb.edu/~joan/MATHQ114/change.htm
References
Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39:10059–10072. doi:10.1016/j.eswa.2012.02.038
Vozalis MG, Margaritis KG (2007) Using SVD and demographic data for the enhancement of generalized collaborative filtering. Inf Sci 177:3017–3037. doi:10.1016/j.ins.2007.02.036
Anand D, Bharadwaj KK (2011) Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities. Expert Syst Appl 38:5101–5109. doi:10.1016/j.eswa.2010.09.141
Chen Y, Wu C, Xie M, Guo X (2011) Solving the sparsity problem in recommender systems using association retrieval. J Comput 6:1896–1902
Zhang J, Lin Y, Lin M, Liu J (2016) An effective collaborative filtering algorithm based on user preference clustering. Appl Intell:1–11
Bobadilla J, Ortega F, Hernando A, Bernal J (2012) A collaborative filtering approach to mitigate the new user cold start problem. Knowl-Based Syst 26:225–238
Lika B, Kolomvatsos K, Hadjiefthymiades S (2014) Facing the cold start problem in recommender systems. Expert Syst Appl 41:2065–2073. doi:10.1016/j.eswa.2013.09.005
Vizine Pereira AL, Hruschka ER (2015) Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowl-Based Syst 82:11–19. doi:10.1016/j.knosys.2015.02.016
Zhang Z, Liu H (2015) Social recommendation model combining trust propagation and sequential behaviors. Appl Intell 43:695–706
Martín-Vicente MI, Gil-Solla A, Ramos-Cabrer M et al (2014) A semantic approach to improve neighborhood formation in collaborative recommender systems. Expert Syst Appl 41:7776–7788
Li Y, Lu L, Xuefeng L (2005) A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce. Expert Syst Appl 28:67–77
Choi K, Suh Y (2013) A new similarity function for selecting neighbors for each target item in collaborative filtering. Knowl-Based Syst 37:146–153
Al-Hassan M, Lu H, Lu J (2015) A semantic enhanced hybrid recommendation approach: a case study of e-Government tourism service recommendation system. Decis Support Syst 72:97–109. doi:10.1016/j.dss.2015.02.001
Shambour Q, Lu J (2012) A trust-semantic fusion-based recommendation approach for e-business applications. Decis Support Syst 54:768–780
Forsati R, Mahdavi M, Shamsfard M, Sarwat M (2014) Matrix factorization with explicit trust and distrust side information for improved social recommendation. ACM Trans Inf Syst (TOIS) 32:17
Mobasher B, Jin X, Zhou Y (2004) Semantically enhanced collaborative filtering on the web. In: Web mining: from web to semantic web. Springer, pp 57–76
Chen R-C, Huang Y-H, Bau C-T, Chen S-M (2012) A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection. Expert Syst Appl 39:3995–4006. doi:10.1016/j.eswa.2011.09.061
Ruotsalo T, Haav K, Stoyanov A et al (2013) SMARTMUSEUM: a mobile recommender system for the Web of Data. Web Semant Sci Serv Agents World Wide Web 20:50–67. doi:10.1016/j.websem.2013.03.001
Capelle M, Hogenboom F, Hogenboom A, Frasincar F (2013) Semantic news recommendation using wordnet and bing similarities. In: Proceedings of the 28th annual ACM symposium on applied computing. ACM, New York, pp 296–302
Gohari FS, Tarokh MJ (2015) New recommender framework: combining semantic similarity fusion and bicluster collaborative filtering. Comput Intell. doi:10.1111/coin.12066
Bogdanov D, Haro M, Fuhrmann F et al (2013) Semantic audio content-based music recommendation and visualization based on user preference examples. Inf Process Manag 49:13–33. doi:10.1016/j.ipm.2012.06.004
Ostuni VC, Di Noia T, Di Sciascio E et al (2015) A semantic hybrid approach for sound recommendation. In: Proceedings of the 24th international conference on world wide web companion. International world wide web conferences steering committee. Republic and Canton of Geneva, Switzerland , pp 85–86
Hwang C-S, Chen Y-P (2007) Using trust in collaborative filtering recommendation. In: New trends in applied artificial intelligence. Springer, pp 1052–1060
Yuan W, Guan D, Lee Y-K et al (2010) Improved trust-aware recommender system using small-worldness of trust networks. Knowl-Based Syst 23:232–238
Lathia N, Hailes S, Capra L (2008) Trust-based collaborative filtering. In: Trust management II. Springer, pp 119–134
Papagelis M, Plexousakis D, Kutsuras T (2005) Alleviating the sparsity problem of collaborative filtering using trust inferences. In: Trust management. Springer, pp 224–239
Yuan W, Shu L, Chao H -C et al (2010) ITARS: trust-aware recommender system using implicit trust networks. Communications, IET 4:1709–1721
O’Donovan J, Smyth B (2005) Trust in recommender systems. In: Proceedings of the 10th international conference on intelligent user interfaces. ACM, pp 167–174
Pitsilis G, Marshall LF (2004) A model of trust derivation from evidence for use in recommendation systems. University of Newcastle upon Tyne, Computing Science
Guo G, Zhang J, Thalmann D (2014) Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowl-Based Syst 57:57–68. doi:10.1016/j.knosys.2013.12.007
Guo G, Zhang J, Thalmann D et al (2014) From ratings to trust: an empirical study of implicit trust in recommender systems. In: Proceedings of the 29th annual ACM symposium on applied computing. ACM, pp 248–253
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, pp 492–508
Haydar C, Roussanaly A, Boyer A (2013) Local trust versus global trust networks in subjective logic. In: 2013 IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT). IEEE, pp 29–36
Dorigo M, Birattari M (2010) Ant colony optimization. In: Encyclopedia of machine learning. Springer, pp 36–39
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B Cybern 26:29–41
Bedi P, Sharma R (2012) Trust based recommender system using ant colony for trust computation. Expert Syst Appl 39:1183–1190
Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1:28–39. doi:10.1109/MCI.2006.329691
M’Hallah R, Alhajraf A (2008) Ant colony optimization for the single machine total earliness tardiness scheduling problem. In: Nguyen NT, Borzemski L, Grzech A, Ali M (eds) New frontiers in applied artificial intelligence. Springer, Berlin, pp 397–407
Soleimani-Pouri M, Rezvanian A, Meybodi MR (2012) Finding a maximum clique using ant colony optimization and particle swarm optimization in social networks. In: Proceedings of the 2012 international conference on advances in social networks analysis and mining (ASONAM 2012). IEEE Computer Society, pp 58–61
Zecchin AC, Simpson AR, Maier HR et al (2006) Application of two ant colony optimisation algorithms to water distribution system optimisation. Math Comput Model 44:451–468. doi:10.1016/j.mcm.2006.01.005
Alba E (2005) Parallel metaheuristics: a new class of algorithms. Wiley
Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adap Inter 12:331–370
Resnick P, Iacovou N, Suchak M et al (1994) GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on Computer supported cooperative work. ACM, pp 175–186
Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst (TOIS) 22:143–177
Ma H, King I, Lyu MR (2007) Effective missing data prediction for collaborative filtering. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 39–46
Blanco-Fernández Y, Pazos-Arias J J, Gil-Solla A et al (2008) A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems. Knowl-Based Syst 21:305–320
Martín-Vicente MI, Gil-Solla A, Ramos-Cabrer M et al (2012) Semantic inference of user’s reputation and expertise to improve collaborative recommendations. Expert Syst Appl 39:8248–8258
Lu J, Shambour Q, Xu Y et al (2013) A web-based personalized business partner recommendation system using fuzzy semantic techniques. Comput Intell 29:37–69
Ma H (2013) An experimental study on implicit social recommendation. In: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 73–82
Yuan W, Guan D, Lee Y-K, Lee S (2011) The small-world trust network. Appl Intell 35:399–410
Fang H, Guo G, Zhang J (2015) Multi-faceted trust and distrust prediction for recommender systems. Decis Support Syst 71:37–47
Bellaachia A, Alathel D (2012) Trust-based ant recommender (T-BAR). In: 2012 6th IEEE international conference intelligent systems (IS). IEEE, pp 130–135
Bellaachia A, Alathel D (2014) DT-BAR : a dynamic ANT recommender to balance the overall prediction accuracy for all users. Academy & Industry Research Collaboration Center (AIRCC):141–151
Liu NN, Zhao M, Xiang E, Yang Q (2010) Online evolutionary collaborative filtering. In: Proceedings of the fourth ACM conference on recommender systems. ACM, pp 95–102
Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. Wiley
Carrer-Neto W, Hernández-Alcaraz M L, Valencia-García R, García-sánchez F (2012) Social knowledge-based recommender system. Application to the movies domain. Expert Syst Appl 39:10990–11000. doi:10.1016/j.eswa.2012.03.025
MacQueen J et al (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth berkeley symposium on mathematical statistics and probability, Oakland, pp 281–297
Jin R, Si L (2004) A study of methods for normalizing user ratings in collaborative filtering. In: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 568–569
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 17:734–749
Cormen TH (2009) Introduction to algorithms. MIT press
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, pp 285–295
Horridge M, Bechhofer S (2011) The OWL API: a Java API for OWL ontologies. Semantic Web 2:11–21
Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst (TOIS) 22:5–53
Cremonesi P, Koren Y, Turrin R (2010) Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the fourth ACM conference on Recommender systems. ACM, pp 39–46
Acknowledgments
We would like to thank Dr. Belmond Yoberd and Professor Nader Bagherzadeh, University of California, Irvine, for their helpful suggestions in editing the paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gohari, F.S., Haghighi, H. & Aliee, F.S. A semantic-enhanced trust based recommender system using ant colony optimization. Appl Intell 46, 328–364 (2017). https://doi.org/10.1007/s10489-016-0830-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-016-0830-y