‎Provenance Based Trust Boosted Recommender System Using Boosted Vector Similarity Measure

Subject Areas : Transactions on Fuzzy Sets and Systems

Dhanalakshmi Teekaraman ¹ (Department of Computer Science and Engineering, Associate Professor, Jerusalem College of Engineering, Chennai, India.)
Sendhilkumar Selvaraju ² (Department of Information Science and Technology, Professor, Anna University, CEG Campus, Chennai, India.)
Mahalakshmi Guruvayur Suryanarayanan ³ (Department of Computer Science and Engineering, Associate Professor, Anna University, CEG Campus, Chennai, India.)

Received: 2023-05-31 Accepted : 2023-10-08 Published : 2023-11-07

Keywords: Social network, Provenance, Trust, Fuzzy rule, Fuzzy vector space, Multi-attribute.,

Abstract :

As users in an online social network are overwhelmed by the abundant amount of information, it is very hard to retrieve the preferred or required content. In this context, an online recommender system helps to filter and recommend content such as people,items or services. But, in a real scenario, people rely more on recommendations from trusted sources than distrusting sources. Though, there are many trust based recommender systems that exist, it lag in prediction error. In order to improve the accuracy of the prediction, this paper proposes a Trust-Boosted Recommender System (TBRS). Since, the provenance derives the trust in a better way than other approaches, TBRS is built from the provenance concept. The proposed recommender system takes the provenance based fuzzy rules which were derived from the Fuzzy Decision Tree. TBRS then computes the multi-attribute vector similarity score and boosts the score with trust weight. This system is tested on the book-review dataset to recommend the top-k trustworthy reviewers.The performance of the proposed method is evaluated in terms of MAE and RMSE. The result shows that the error value of boosted similarity is lesser than without boost. The reduced error rates of the Jaccard, Dice and Cosine similarity measures are 18%, 15% and 7% respectively. Also, when the model is subjected to failure analysis, it gives better performance for unskewed data than slewed data. The models fbest, average and worst case predictions are 90%, 50% and <23% respectively.

References:

[1] Abdul-Rahman A, Hailes S. Supporting trust in virtual communities. In: Proceedings of the 33rd Hawaii International Conference on System Sciences. January 7, Maui, HI, USA. 2000. p.1769-1777. DOI: 10.1109/HICSS.2000.926814
[2] Andersen R, Borgs C, Chayes J, Feige U, Flaxman A, Kalai A, Mirrokni V, Tennenholtz M. Trust-based recommendation systems: An axiomatic approach. In: Proceedings of the 17th ACM International Conference on World Wide Web, April 2125, Beijing, China. 2008. p.199-208. DOI: https://doi.org/10.1145/1367497.1367525
[3] Arafeh M, Ceravolo P, Mourad A, Damiani E, Bellini E. Ontology based recommender system using social network data. Future Generation Computer Systems. 2021; 115: 769779. DOI: https://doi.org/10.1016/j.future.2020.09.030
[4] Avesani P, Massa P, Tiella R. Moleskiing.it: A trust-aware recommender system for ski mountainteering. International Journal for Infonomics. 2005; 1-10. DOI: https://api.semanticscholar.org/CorpusID:10873049
[5] Barbier GB, Huan Liu. Finding Provenance Data in Social Media. Arizona State University; 2011.
[6] Bellaachia A, Alathel D. Improving the recommendation accuracy for cold start users in trust-based recommender systems. International Journal of Computer and Communication Engineering. 2016; 5(3): 206-214. DOI: 10.17706/ijcce.2016.5.3.206-214
[7] Channappagoudar NB, Singh R. Trust based recommendation system using knowledge graph (KGTRS). In: ICIDSSD 2022: Proceedings of the 3rd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2022, 24-25 March 2022, New Delhi, India. 2023. p. 25-36. DOI: https://eudl.eu/pdf/10.4108/eai.24-3-2022.2318767
[8] Choi SS, Cha SH, Tappert CC. A survey of binary similarity and distance measures. Journal of Systemics, Cybernetics and Informatics. 2010; 8(1): 43-48. DOI: https://www.iiisci.org/Journal/pdv/sci/pdfs/GS315JG.pdf

[9] Duricic T, Lacic E, Kowald D, Lex E. Trust based collaborative  ltering: Tackling the cold start problem using regular equivalence. In: Proceedings of the 12th ACM Conference on Recommender Systems. 2018. p.446-450. DOI: https://dl.acm.org/doi/10.1145/3240323.3240404
[10] Falcone R, Pezzulo G, Castelfranchi C. A fuzzy approach to a belief based trust computation. In: Lecture Notes in Arti cial Intelligence. 2003. p.73-86. DOI: https://doi.org/10.1007/3-540-36609-1_7
[11] Faridani V, Jalali M, Jahan MV. Collaborative  ltering-based recommender systems by e ective trust. International Journal of Data Science and Analytics. 2017; 3(4): 297-307. DOI: https://link.springer.com/article/10.1007/s41060-017-0049-y
[12] George G, Lal AM. A personalized approach to course recommendation in higher education. International Journal on Semantic Web and Information Systems. 2021; 17(2): 100-114. DOI: 10.4018/IJSWIS.2021040106
[13] Golbeck JA. Computing and Applying Trust in Web-Based Social Networks. United States: University of Maryland at College Park; 2005.
[14] Hang CW, Singh MP. Trust based recommendation based on graph similarities. 2010. DOI: http://www.csc.ncsu.edu/faculty/mpsingh/papers/mas/aamas-trust-10-graph.pdf
[15] Janowicz K. Trust and provenance you can't have one without the other. Muenster, Germany. 2009. DOI: https://api.semanticscholar.org/CorpusID:13276942
[16] Jiang L, Cheng Y, Yang L, Li J, Yan H, Wang X. A trust-based collaborative  ltering algorithm for E-commerce recommendation system. Journal of Ambient Intelligence and Humanized Computing. 2019; 10(8): 30233034. DOI: https://doi.org/10.1007/s12652-018-0928-7
[17] Li P, Li T,Wang X, Zhang S, Jiang Y, Tang Y. Scholar recommendation based on high-order propagation of knowledge graphs. International Journal on Semantic Web and Information Systems. 2022; 18(1): 1-19. DOI: https://doi.org/10.4018/IJSWIS.297146

[18] Liao M, Sundar SS, Walther JB. User trust in recommendation systems: A comparison of content-based, collaborative and demographic  ltering. In: Proceedings of the 2022 CHI Conference on Human Factors  in Computing Systems (CHI '22), Association for Computing Machinery, April 29 - May 5, New York, USA. 2022. p.114. DOI: https://doi.org/10.1145/3491102.3501936
[19] Linden G, Smith B, York J. Amazon.com recommendations: Item-to-item collaborative  ltering. Journal of IEEE Internet Computing. 2003; 7(1): 76-80. DOI: 10.1109/MIC.2003.1167344
[20] Mandal S, Maiti A. Heterogeneous trust-based social recommendation via reliable and informative motif-based attention. In: 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy. 2022 p.1-8. DOI: 10.1109/IJCNN55064.2022.9892977
[21] Massa P, Avesani P. Trust-aware recommender systems. In: Proceedings of the  rst ACM Conference on Recommender Systems, October 19-20, Minneapolis MN USA. 2007. p.17-24. DOI: https://doi.org/10.1145/1297231.1297235
[22] Olaru C, Wehenkel L. A complete fuzzy decision tree technique. Journal of Fuzzy Sets and Systems.
2003; 138(2): 221-254. DOI: https://doi.org/10.1016/S0165-0114(03)00089-7
[23] Parvin H, Moradi P, Esmaeili Sh. TCFACO: Trustaware collaborative  ltering method based on ant colony optimization. Journal of Expert Systems with Application. 2019; 118: 152-168. DOI: https://doi.org/10.1016/j.eswa.2018.09.045
[24] Rad D, Cuc LD, Feher A, Joldes CSR, Batca-Dumitru GC, Sendroiu C, Almasi RC, Chis S, Popescu MG. The in uence of social strati cation on trust in recommender systems. Electronics. 2023; 12(10): 2160. DOI: 10.3390/electronics12102160 
[25] Richa, Bedi P. Trust and distrust based cross-domain recommender system. Applied Arti cial Intelligence. 2021; 35(4): 326-351. DOI: https://doi.org/10.1080/08839514.2021.1881297
[26] Salloum G, Tekli J. Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem. Soft Computing. 2022; 26: 25612585. DOI: https://doi.org/10.1007/s00500-021-06400-1
[27] Sinha RR, Swearingen K. Comparing recommendations made by online systems and friends. In: Proceedings of the DELOS-NSF Workshop on Personalisation and Recommender Systems in Digital Libraries. 2001. DOI: https://api.semanticscholar.org/CorpusID:15526356
[28] Smith NJJ. Vagueness and Degrees of Truth. Oxford, 2008; online edn, Oxford Academic, 1 Jan. 2009. DOI: https://doi.org/10.1093/acprof:oso/9780199233007.001.0001
[29] Smyth B, O'Donovan J. Trust in recommender systems. In: Proceedings of 10th international conference on intelligent user interfaces, January 10-13, San Diego, California, USA. 2005. p.167-174. DOI: https://doi.org/10.1145/1040830.1040870
[30] Sudha Ram, Jun Liu. Book on Active conceptual modeling of learning. Peter P. Chen, Leah Y. Wong (eds.).Lecture Notes in Computer Science. Springer-Verlag Berlin, Heidelberg; 2007; 17-29. DOI: https://link.springer.com/book/10.1007/978-3-540-77503-4

[31] Teekaraman D, Sendhilkumar S, Mahalakshmi GS. Semantic provenance based trustworthy user classi cation on book-based social network using Fuzzy decision tree. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2020; 28(1): 47-77. DOI: https://doi.org/10.1142/S0218488520500038
[32] Victor P, Cornellis C , Cock MD, Teredesai AM. Trust and distrust based recommendations for controversial reviews. IEEE Intelligent Systems. 2011; 26: 4855. DOI: 10.1109/MIS.2011.22
[33] Xiao J, Liu X, Zeng J, Cao Y, Feng Z. Recommendation of healthcare services based on an embedded user pro le model, International Journal on Semantic Web and Information Systems. 2022; 18(1): 1-21. DOI: 10.4018/IJSWIS.313198
[34] Xue K, Wang J. Collaborative  ltering recommendation algorithm for user interest and relationship based on score matrix. In: 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms. 2018. p.217-221. DOI: 10.2991/mmsa-18.2018.49
[35] Yang B, Lei Y, Liu J, Li W. Social collaborative  ltering by trust. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017; 39(8): 1633-1647. DOI: 10.1109/TPAMI.2016.2605085
[36] Ye L,Wu C, Li M. Collaborative  ltering recommendation based on trust model with fused similar factor. In: MATEC Web of Conferences. 2017. p.1-7. DOI: https://doi.org/10.1051/matecconf/201713900010