بهبود دقت سيستمهای پیشنهاددهنده با تخمین اعتماد آگاه از زمان، مکان و زمینه بر اساس خوشه بندی و توزیع بتا
الموضوعات :سمانه شیبانی 1 , حسن شاکری 2 , رضا شیبانی 3
1 - گروه مهندسي كامپيوتر، دانشگاه آزاد اسلامی واحد مشهد
2 - گروه مهندسي كامپيوتر، دانشگاه آزاد اسلامی واحد مشهد
3 - گروه مهندسی کامپیوتر دانشگاه آزاد اسلامی واحد مشهد
الکلمات المفتاحية: اعتماد, پيشنهاد آگاه از زمينه, توزيع بتا, خوشهبندي, سيستمهاي پيشنهاددهنده,
ملخص المقالة :
در دهههاي اخير رويكرد محاسبه و اعمال اعتماد بين كاربران در طراحي سيستمهاي پيشنهاددهنده مورد توجه محققان قرار گرفته است. با وجود اين، اغلب سيستمهاي پيشنهاددهنده مبتني بر اعتماد فقط از يك فاكتور براي تخمين مقدار اعتماد استفاده ميكنند. در اين مقاله يك رويكرد چندفاكتوري براي تخمين اعتماد بين كاربران سيستمهاي پيشنهاددهنده ارائه ميشود. در طرح پيشنهادي، ابتدا كاربران سيستم براساس شباهت مبتني بر اطلاعات دموگرافيك و تاريخچه ارزشيابيها خوشهبندي ميشوند. براي تخمين ارزشيابي كاربر فعال به يك آيتم خاص، مقدار اعتماد بين او و ساير كاربران همخوشهاش با درنظرگرفتن فاكتورهاي زمان، مكان، و زمينه ارزشيابي محاسبه ميشود. براي اين منظور، ما الگوريتمي مبتني بر توزيع بتا معرفي ميكنيم. يك معيار مبتني بر درخت جديد براي محاسبه شباهت معنايي بين زمينهها مورد استفاده قرار ميگيرد. در نهايت، ارزشيابي كاربر فعال با استفاده از ميانگينگيري وزني تخمين زده ميشود كه مقادير اعتماد به عنوان وزن در ميانگينگيري منظور ميشوند. طرح پيشنهادي بر روي سه مجموعهداده مطرح اجرا شده و ارزيابي و مقايسه نشان ميدهد كه اين طرح نتايج بهتري از نظر ملاكهاي دقت و كارآمدي نسبت به روشهاي موجود ارائه ميكند.
1. Yao, L., Xu, Z., Zhou, X., & Lev, B., Synergies Between Association Rules and Collaborative Filtering in Recommender System: An Application to Auto Industry. Data Science and Digital Business, 2019: p. 16.
2. Rubens, N., et al., Active learning in recommender systems, in Recommender systems handbook. 2015, Springer. p. 809-846.
3. Li, Y.-M., C.-T. Wu, and C.-Y. Lai, A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship. Decision Support Systems, 2013. 55(3): p. 740-752.
4. Savage, N.S., et al., I’m feeling loco: A location based context aware recommendation system, in Advances in Location-Based Services. 2012, Springer. p. 37-54.
5. Bączkiewicz, A., et al., Methodical Aspects of MCDM Based E-Commerce Recommender System. Journal of Theoretical and Applied Electronic Commerce Research, 2021. 16(6): p. 2192-2229.
6. Elahi, M., et al., User Preference Elicitation, Rating Sparsity and Cold Start. 2018.
7. Antolić, G. and L. Brkić. Recommender system based on the analysis of publicly available data. in 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). 2017. IEEE.
8. Wangwatcharakul, C. and S. Wongthanavasu, A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution. Expert Systems with Applications, 2021. 185: p. 115626.
9. Nilashi, M., O. Ibrahim, and K. Bagherifard, A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications, 2018. 92: p. 507-520.
10. Zheng, X.-L., et al., A hybrid trust-based recommender system for online communities of practice. IEEE Transactions on Learning Technologies, 2015. 8(4): p. 345-356.
11. Sani, N.S. and F.N. Tabriz, A new strategy in trust-based recommender system using k-means clustering. International Journal of Advanced Computer Science And Applications, 2017. 8(9): p. 152-156.
12. Nobahari, V., M. Jalali, and S.J.S. Mahdavi, ISoTrustSeq: a social recommender system based on implicit interest, trust and sequential behaviors of users using matrix factorization. Journal of Intelligent Information Systems, 2019. 52(2): p. 239-268.
13. AlBanna, B., et al., Interest aware location-based recommender system using geo-tagged social media. ISPRS International Journal of Geo-Information, 2016. 5(12): p. 245.
14. Celdrán, A.H., et al., Design of a recommender system based on users’ behavior and collaborative location and tracking. Journal of Computational Science, 2016. 12: p. 83-94.
15. Khazaei, E. and A. Alimohammadi, An automatic user grouping model for a group recommender system in location-based social networks. ISPRS international journal of geo-information, 2018. 7(2): p. 67.
16. Gao, H., et al. Content-aware point of interest recommendation on location-based social networks. in Twenty-ninth AAAI conference on Artificial Intelligence. 2015.
17. Tahmasbi, H., M. Jalali, and H. Shakeri. Modeling temporal dynamics of user preferences in movie recommendation. in 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE). 2018. IEEE.
18. Kefalas, P. and Y. Manolopoulos, A time-aware spatio-textual recommender system. Expert Systems with Applications, 2017. 78: p. 396-406.
19. Fard, K.B., et al., Recommender System Based on Semantic Similarity. International Journal of Electrical & Computer Engineering (2088-8708), 2013. 3(6).
20. Urena, R., et al., A review on trust propagation and opinion dynamics in social networks and group decision making frameworks. Information Sciences, 2019. 478: p. 461-475.
21. Ghavipour, M. and M.R. Meybodi, A dynamic algorithm for stochastic trust propagation in online social networks: Learning automata approach. Computer Communications, 2018. 123: p. 11-23.
22. Xue, H., et al., Content-aware trust propagation toward online review spam detection. Journal of Data and Information Quality (JDIQ), 2019. 11(3): p. 1-31.
23. Ying, H., et al., Time-aware metric embedding with asymmetric projection for successive POI recommendation. World Wide Web, 2019. 22(5): p. 2209-2224.
24. Rafailidis, D. and A. Nanopoulos, Modeling users preference dynamics and side information in recommender systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015. 46(6): p. 782-792.
25. Adomavicius, G. and A. Tuzhilin, Context-aware recommender systems, in Recommender systems handbook. 2011, Springer. p. 217-253.
26. Lathia, N., et al. Temporal diversity in recommender systems. in Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. 2010.
27. Xiong, L., et al. Temporal collaborative filtering with bayesian probabilistic tensor factorization. in Proceedings of the 2010 SIAM international conference on data mining. 2010. SIAM.
28. Roy, F., A Comparative Analysis of Different Trust Metrics in User-User Trust-Based Recommender System. 2020.
29. Bedi, P., Combining trust and reputation as user influence in cross domain group recommender system (CDGRS). Journal of Intelligent & Fuzzy Systems, 2020. 38(5): p. 6235-6246.
30. Richa and P. Bedi, Trust and Distrust based Cross-domain Recommender System. Applied Artificial Intelligence, 2021. 35(4): p. 326-351.
31. El Yebdri, Z., et al., Context-aware recommender system using trust network. Computing, 2021: p. 1-19.
32. Asani, E., H. Vahdat-Nejad, and J. Sadri, Restaurant recommender system based on sentiment analysis. Machine Learning with Applications, 2021. 6: p. 100114.
33. Cardoso, I.M.G., Vulcont: A Recommender System based on Contexts History Ontology. 2017.
34. Seo, Y.-D. and Y.-S. Cho, Point of interest recommendations based on the anchoring effect in location-based social network services. Expert Systems with Applications, 2021. 164: p. 114018.
35. Milias, V. and A. Psyllidis, Assessing the influence of point-of-interest features on the classification of place categories. Computers, Environment and Urban Systems, 2021. 86: p. 101597.
36. Han, P., et al., Point-of-interest recommendation with global and local context. IEEE Transactions on Knowledge and Data Engineering, 2021.
37. Agrawal, S., D. Roy, and M. Mitra, Tag embedding based personalized point of interest recommendation system. Information Processing & Management, 2021. 58(6): p. 102690.
38. Fang, W., et al., A resilient trust management scheme for defending against reputation time-varying attacks based on BETA distribution. Science China Information Sciences, 2017. 60(4): p. 1-11.
39. Moe, M.E., B.E. Helvik, and S.J. Knapskog. Comparison of the beta and the hidden markov models of trust in dynamic environments. in IFIP International Conference on Trust Management. 2009. Springer.
40. Wu, X., et al., BLTM: beta and LQI based trust model for wireless sensor networks. IEEE Access, 2019. 7: p. 43679-43690.
41. Wu, Z. and M. Palmer. V Verbs semantics and lexical selection. in Proceedings of the 32nd annual meeting on Association for Computational Linguistics. 1994.
42. Yelp Challenge Dataset. 2018.
43. Liu, Y., et al., An experimental evaluation of point-of-interest recommendation in location-based social networks. Proceedings of the VLDB Endowment, 2017. 10(10): p. 1010-1021.
44. Cho, E., S.A. Myers, and J. Leskovec. Friendship and mobility: user movement in location-based social networks. in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. 2011.
45. Ardissono, L. and N. Mauro, A compositional model of multi-faceted trust for personalized item recommendation. Expert Systems with Applications, 2020. 140: p. 112880.
