Abstract
Collaborative filtering (CF) is the most popular recommendation approach that has been extensively employed in recommender systems. However, it suffers from some weaknesses, including problems with cold start users, data sparsity and difficulty in detecting malicious users. Trust-based recommender systems can overcome these weaknesses by using the ratings of trusted users. However, since users often provide few trust statements, trust networks are typically sparse and therefore the cold start and sparsity problems still remain. In this paper, we use the positive correlation between trust and interest similarity to enrich trust network by similarity relations and propose a stochastic trust propagation-based method, called LTRS, which utilizes the enriched trust network to provide enhanced recommendations. In comparison with existing recommender systems combining trust and similarity information, the proposed system (1) incorporates both trust and similarity relations in the trust propagation process and, in this way, increases the coverage and accuracy of predictions; and (2) addresses the dynamic nature of both trust and similarity by modelling the enriched network as a stochastic graph, and continuously captures their variations during the recommendation process and not at fixed intervals. The experimental results indicate that the proposed method can significantly improve the recommendation accuracy and coverage.
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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
Gao L, Li C (2008) Hybrid personalized recommended model based on genetic algorithm. In: 4th international conference on wireless communications, networking and mobile computing, 2008. WiCOM’08. IEEE, pp 1–4
Zhong J, Li X (2010) Unified collaborative filtering model based on combination of latent features. Expert Syst Appl 37:5666–5672
Luo X, Xia Y, Zhu Q (2012) Incremental collaborative filtering recommender based on regularized matrix factorization. Knowledge-Based Syst 27:271–280
Wang Y, Deng J, Gao J, Zhang P (2017) A hybrid user similarity model for collaborative filtering. Inf Sci (Ny) 418:102–118
Ren L, Wang W (2018) An SVM-based collaborative filtering approach for Top-N web services recommendation. Futur Gener Comput Syst 78:531–543
Sinha RR, Swearingen K (2001) Comparing Recommendations Made by Online Systems and Friends. In: DELOS workshop: personalisation and recommender systems in digital libraries
Ziegler C-N, Lausen G (2004) Analyzing correlation between trust and user similarity in online communities. In: ITrust. Springer, pp 251–265
Massa P, Avesani P (2004) Trust-aware collaborative filtering for recommender systems. CoopIS/DOA/ODBASE (1) 3290:492–508
Arazy O, Kumar N, Shapira B (2009) Improving social recommender systems. IT Prof 11
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
Yan S, Zheng X, Chen D, Wang Y (2013) Exploiting two-faceted web of trust for enhanced-quality recommendations. Expert Syst Appl 40:7080–7095
Guo G, Zhang J, Yorke-Smith N (2015) Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems. Knowledge-Based Syst 74:14–27
Moradi P, Ahmadian S (2015) A reliability-based recommendation method to improve trust-aware recommender systems. Expert Syst Appl 42:7386–7398
Mao M, Lu J, Zhang G, Zhang J (2017) Multirelational social recommendations via multigraph ranking. IEEE Trans Cybern 47:4049–4061. https://doi.org/10.1109/TCYB.2016.2595620
Sheugh L, Alizadeh SH (2018) A novel 2D-Graph clustering method based on trust and similarity measures to enhance accuracy and coverage in recommender systems. Inf Sci (Ny) 432:210–230
Deng X, Zhong Y, Lü L, et al. (2017) A general and effective diffusion-based recommendation scheme on coupled social networks. Inf Sci (Ny) 417:420–434
Kalaï A, Zayani CA, Amous I, et al. (2018) Social collaborative service recommendation approach based on user’s trust and domain-specific expertise. Futur Gener Comput Syst 80:355–367
Ziegler C -N, Golbeck J (2007) Investigating interactions of trust and interest similarity. Decis Support Syst 43:460–475
Bhuiyan T (2013) Trust for intelligent recommendation. Springer, Berlin
Golbeck J (2009) Trust and nuanced profile similarity in online social networks. ACM Trans Web 3:12
Shambour Q, Lu J (2011) A hybrid trust-enhanced collaborative filtering recommendation approach for personalized government-to-business e-services. Int J Intell Syst 26:814–843
Shambour Q, Lu J (2012) A trust-semantic fusion-based recommendation approach for e-business applications. Decis Support Syst 54:768–780
Uddin MG, Zulkernine M, Ahamed SI (2008) CAT: a context-aware trust model for open and dynamic systems. In: Proceedings of the 2008 ACM symposium on Applied computing. ACM, pp 2024–2029
Gohari FS, Aliee FS, Haghighi H (2018) A new confidence-based recommendation approach: Combining trust and certainty. Inf Sci (Ny) 422:21–50
Shambour Q, Lu J (2015) An effective recommender system by unifying user and item trust information for B2B applications. J Comput Syst Sci 81:1110–1126
Bedi P, Sharma R (2012) Trust based recommender system using ant colony for trust computation. Expert Syst Appl 39:1183–1190
Ghavipour M, Meybodi MR (2018) A dynamic algorithm for stochastic trust propagation in online social networks: Learning automata approach. Comput Commun 123:11–23. https://doi.org/10.1016/j.comcom.2018.04.004
Protasiewicz J, Pedrycz W, Kozłowski M, et al. (2016) A recommender system of reviewers and experts in reviewing problems. Knowledge-Based Syst 106:164–178
Lops P, De Gemmis M, Semeraro G (2011) Content-based recommender systems: State of the art and trends. In: Recommender systems handbook. Springer, pp 73–105
Martinez-Romo J, Araujo L (2012) Updating broken web links: An automatic recommendation system. Inf Process Manag 48:183–203
Pera MS, Ng Y -K (2013) A group recommender for movies based on content similarity and popularity. Inf Process Manag 49:673–687
Bobadilla J, Ortega F, Hernando A, Bernal J (2012) A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-Based Syst 26:225–238
Bobadilla J, Hernando A, Ortega F, Bernal J (2011) A framework for collaborative filtering recommender systems. Expert Syst Appl 38:14609–14623
Bobadilla J, Hernando A, Ortega F, Gutiérrez A (2012) Collaborative filtering based on significances. Inf Sci (Ny) 185:1–17
Altingovde IS, Subakan ÖN, Ulusoy Ö (2013) Cluster searching strategies for collaborative recommendation systems. Inf Process Manag 49:688–697
Formoso V, FernáNdez D, Cacheda F, Carneiro V (2013) Using profile expansion techniques to alleviate the new user problem. Inf Process Manag 49:659–672
Choi IY, Oh MG, Kim JK, Ryu YU (2016) Collaborative filtering with facial expressions for online video recommendation. Int J Inf Manage 36:397–402
Wang H, Shao S, Zhou X, et al. (2016) Preference recommendation for personalized search. Knowledge-Based Syst 100:124–136
Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In: Recommender systems handbook. Springer, pp 1–35
Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:4
Linden G, Smith B, York J (2003) Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput 7:76–80
Lemire D (2005) Scale and translation invariant collaborative filtering systems. Inf Retr Boston 8:129–150
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
Symeonidis P, Nanopoulos A, Manolopoulos Y (2009) MoviExplain: a recommender system with explanations. In: Proceedings of the third ACM conference on Recommender systems. ACM, pp 317–320
Park M-H, Hong J-H, Cho S-B (2007) Location-based recommendation system using bayesian user’s preference model in mobile devices. In: International Conference on Ubiquitous Intelligence and Computing. Springer, pp 1130–1139
Roh TH, Oh KJ, Han I (2003) The collaborative filtering recommendation based on SOM cluster-indexing CBR. Expert Syst Appl 25:413–423
Yager RR (2003) Fuzzy logic methods in recommender systems. Fuzzy Sets Syst 136:133–149
Gurini DF, Gasparetti F, Micarelli A, Sansonetti G (2018) Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization. Futur Gener Comput Syst 78:430–439
Bobadilla J, Serradilla F (2009) The effect of sparsity on collaborative filtering metrics. In: Proceedings of the 20th Australasian conference on australasian database-volume, vol 92. Australian Computer Society, Inc., pp 9–18
Rashid AM, Karypis G, Riedl J (2008) Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explor Newsl 10:90–100
Kim H -N, El-Saddik A, Jo G -S (2011) Collaborative error-reflected models for cold-start recommender systems. Decis Support Syst 51:519–531
Leung CW, Chan SC, Chung F (2008) An empirical study of a cross-level association rule mining approach to cold-start recommendations. Knowledge-Based Syst 21:515–529
Chirita P-A, Nejdl W, Zamfir C (2005) Preventing shilling attacks in online recommender systems. In: Proceedings of the 7th annual ACM international workshop on Web information and data management. ACM, pp 67–74
Lam SK, Riedl J (2004) Shilling recommender systems for fun and profit. In: Proceedings of the 13th international conference on World Wide Web. ACM, pp 393–402
O’Mahony M, Hurley N, Kushmerick N, Silvestre G (2004) Collaborative recommendation: A robustness analysis. ACM Trans Internet Technol 4:344–377
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
Lee DH, Brusilovsky P (2009) Does trust influence information similarity? Recomm Syst Soc Web 10
Salganik MJ, Dodds PS, Watts DJ (2006) Experimental study of inequality and unpredictability in an artificial cultural market. Science (80-) 311:854–856
Bonhard P, Sasse MA (2006) Knowing me, knowing you—Using profiles and social networking to improve recommender systems. BT Technol J 24:84–98
He J, Chu WW (2010) A social network-based recommender system (SNRS). In: Data mining for social network data. Springer, pp 47–74
Golbeck J (2006) Generating predictive movie recommendations from trust in social networks. In: International Conference on Trust Management. Springer, pp 93–104
Avesani P, Massa P, Tiella R (2005) A trust-enhanced recommender system application: Moleskiing. In: Proceedings of the 2005 ACM symposium on Applied computing. ACM, pp 1589–1593
Staab S, Bhargava B, Leszek L, et al. (2004) The pudding of trust: Managing the dynamic nature of trust. IEEE Intell Syst 19:74–88
Hwang C-S, Chen Y-P (2007) Using trust in collaborative filtering recommendation. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, pp 1052–1060
Yuan W, Shu L, Chao H -C, et al. (2010) ITARS: trust-aware recommender system using implicit trust networks. IET Commun 4:1709–1721
Eirinaki M, Louta MD, Varlamis I (2014) A trust-aware system for personalized user recommendations in social networks. IEEE Trans Syst Man Cybern Syst 44:409–421
O’Donovan J (2009) Capturing trust in social web applications. In: Computing with social Trust. Springer, pp 213–257
Shambour QY (2012) Hybrid recommender systems for personalized government-to-business e-services
Golbeck JA (2005) Computing and applying trust in web-based social networks. https://doi.org/10.1017/CBO9781107415324.004 https://doi.org/10.1017/CBO9781107415324.004
Narendra KS, Thathachar MAL (2012) Learning automata: an introduction. Courier Corporation
Thathachar MAL, Sastry PS (2011) Networks of learning automata: Techniques for online stochastic optimization. Springer Science & Business Media, Berlin
Ghavipour M, Meybodi MR (2017) A streaming sampling algorithm for social activity networks using fixed structure learning automata. Appl Intell. https://doi.org/10.1007/s10489-017-1005-1
Ghavipour M, Meybodi MR (2017) Irregular cellular learning automata-based algorithm for sampling social networks. Eng Appl Artif Intell 59:244–259. https://doi.org/10.1016/j.engappai.2017.01.004 https://doi.org/10.1016/j.engappai.2017.01.004
Ghavipour M, Meybodi MR (2016) An adaptive fuzzy recommender system based on learning automata. Electron Commer Res Appl 20:105–115. https://doi.org/10.1016/j.elerap.2016.10.002
Ghavipour M, Meybodi MR (2017) Trust propagation algorithm based on learning automata for inferring local trust in online social networks. Knowledge-Based Syst. https://doi.org/10.1016/j.knosys.2017.06.034 https://doi.org/10.1016/j.knosys.2017.06.034
Beigy H, Meybodi MR (2006) Utilizing distributed learning automata to solve stochastic shortest path problems. Int J Uncertainty Fuzziness Knowl-Based Syst 14:591–615
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, pp 397– 406
Kant V, Bharadwaj KK (2013) Fuzzy computational models of trust and distrust for enhanced recommendations. Int J Intell Syst 28:332–365
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. ACM, pp 93–102
Richardson M, Agrawal R, Domingos P (2003) Trust management for the semantic web. In: International semantic Web conference. Springer, pp 351–368
Jiang W, Wu J, Wang G (2015) On selecting recommenders for trust evaluation in online social networks. ACM Trans Internet Technol 15:14
Jiang W, Wu J, Li F, et al. (2016) Trust Evaluation in Online Social Networks Using Generalized Network Flow. IEEE Trans Comput 65:952–963
Shekarpour S, Katebi SD (2010) Modeling and evaluation of trust with an extension in semantic web. Web Semant Sci Serv Agents World Wide Web 8:26–36
Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Ijcai. pp 1137–1145
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Ghavipour, M., Meybodi, M.R. Stochastic trust network enriched by similarity relations to enhance trust-aware recommendations. Appl Intell 49, 435–448 (2019). https://doi.org/10.1007/s10489-018-1289-9
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DOI: https://doi.org/10.1007/s10489-018-1289-9