پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 620 |
| تعداد مقالات | 9,114 |
| تعداد مشاهده مقاله | 67,368,495 |
| تعداد دریافت فایل اصل مقاله | 7,856,034 |
ارائه سیستم جدید توصیه عادلانه گروهی مبتنی بر تأثیر اعضا و رهبر | ||
| مدل سازی در مهندسی | ||
| دوره 22، شماره 77، شهریور 1403، صفحه 221-243 اصل مقاله (1.09 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2023.31390.2507 | ||
| نویسندگان | ||
| مصطفی سبزه کار* 1؛ بنت الهدی موذنی2 | ||
| 1استادیار، گروه مهندسی کامپیوتر، دانشگاه صنعتی بیرجند، بیرجند، ایران | ||
| 2کارشناس ارشد، گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی، واحد بیرجند، بیرجند، ایران | ||
| تاریخ دریافت: 10 مرداد 1402، تاریخ بازنگری: 18 آذر 1402، تاریخ پذیرش: 22 آذر 1402 | ||
| چکیده | ||
| در یک سیستم توصیهگر گروهی تلاش میشود ارائه توصیه به گروهی از افراد به‌جای یک فرد صورت پذیرد. در این سیستمها نظرات تمام اعضای گروه در تصمیمگیری مؤثر بوده و سعی میشود تا باوجود متفاوت بودن ترجیحات شخصی، بهترین انتخاب فراهم آید. این مقاله، سعی در ارائه یک سیستم توصیهگر گروهی دارد که بتواند ارتباط بین کاربران و درنهایت تأثیر هر کاربر بر گروه را مشخص کرده و سپس با توجه به این ارتباطات، بهترین توصیه را به پیشنهاد دهد. همچنین، معیاری جدید برای تعیین رهبر در گروه تعیین میشود که با توجه به میزان اعتماد، شباهت و تعلق و وابستگی کاربران به گروه، رهبر آن گروه را مشخص نماید. بعلاوه، معیار جدیدی برای ارائه توصیه عادلانه به گروه ارائه‌شده که با استفاده از آن آیتمهایی به کاربران توصیه شود که بیشترین نظر مثبت در بین همه اعضا گروه داشته باشد. الگوریتم پیشنهادی در دو بخش با الگوریتمهای مشابه در این زمینه، مقایسه شده است. در بخش ارزیابی رتبه‌های تعیین شده، دقت روش پیشنهادی در همه حالات نزدیک به 100% به دست آمد که به نسبت روش‌های مورد مقایسه به طور متوسط بهبود 5 درصدی را گزارش نمود. در بخش ارزیابی توصیه‌ها نیز از معیارهای شناخته شده nDCG، رضایت گروهی و عدالت استفاده شد که روش پیشنهادی با در نظر گرفتن تعداد توصیه‌های مختلف به طور متوسط در هریک از معیارهای ذکر شده در حالت میانگین به ترتیب 41%، 35% و 38% بهبود یافته است. | ||
| کلیدواژه‌ها | ||
| سیستم توصیهگر گروهی؛ عدالت؛ تابع تجمیع | ||
| عنوان مقاله [English] | ||
| A Fair Group Recommendation System Based on Members and Leader Influences | ||
| نویسندگان [English] | ||
| Mostafa Sabzekar1؛ Bentolhoda Moazeni2 | ||
| 1Assistant Professor, Department of Computer Engineering, Birjand University of Technology, Birjand, Iran | ||
| 2MSc, Department of Computer Engineering, Islamic Azad University, Birjand Branch, Birjand, Iran | ||
| چکیده [English] | ||
| In a group recommender system, the effort is made to provide recommendations to a group of individuals rather than a single person. In these systems, the opinions of all group members are influential in decision-making, aiming to provide the best choice despite different personal preferences. This article attempts to present a group recommender system capable of identifying the relationship among users and eventually determining the influence of each user on the group, subsequently offering the best recommendations based on these connections. Moreover, a new criterion for determining leadership in the group is introduced, which identifies the leader of the group based on the level of trust, similarity, belongingness, and dependence of users on the group. Additionally, a novel criterion for delivering fair recommendations to the group is proposed, suggesting items to users with the most positive feedback among all group members. The proposed algorithm is compared with similar algorithms in this domain in two sections. In the evaluation section of assigned rankings, the accuracy of the proposed method was close to 100% in all cases, reporting an average improvement of 5% compared to the compared methods. In the recommendation evaluation section, well-known criteria such as nDCG, group satisfaction, and fairness were used, where the proposed method showed an average improvement of 41%, 35%, and 38%, respectively, considering the number of diverse recommendations in each of the mentioned criteria on average. | ||
| کلیدواژه‌ها [English] | ||
| Group recommender systems, Fairness, Aggregation function | ||
| مراجع | ||
|
[1] L. Baltrunas, T. Makcinskas, and F. Ricci. "Group recommendations with rank aggregation and collaborative filtering." In Proceedings of the fourth ACM conference on Recommender systems, pp. 119-126. 2010. [2] P. Nagaraj, and P. Deepalakshmi. "An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis." International Journal of Imaging Systems and Technology 32, no. 4 (2022): 1373-1396. [3] Le. Gamidullaeva, A. Finogeev, M. Kataev, and L. Bulysheva. "A design concept for a tourism recommender system for regional development." Algorithms 16, no. 1 (2023): 58. [4] A. Poulose, A.P. Valappil, and J. Sebastian. "Medication recommender system for healthcare solutions." Journal of Information and Optimization Sciences 43, no. 5 (2022): 1073-1080. [5] W.K. Cheng, W.C. Leong, J.S. Tan, Z.W. Hong, and Y.L. Chen. "Affective recommender system for pet social network." Sensors 22, no. 18 (2022): 6759. [6] R.K. Patel, K. Aparna, S. Tanwar, W.C. Hong, and R. Sharma. "AI-empowered recommender system for renewable energy harvesting in smart grid system." IEEE Access 10 (2022): 24316-24326.. [7] R. Burke, A. Felfernig, and M.H. Göker. "Recommender systems: An overview." Ai Magazine 32, no. 3 (2011): 13-18. [8] R. Burke. "Hybrid recommender systems: Survey and experiments." User modeling and user-adapted interaction 12 (2002): 331-370. [9] S. Dara, C.R. Chowdary, and C. Kumar. "A survey on group recommender systems." Journal of Intelligent Information Systems 54, no. 2 (2020): 271-295. [10] S. Zhang, L. Yao, A. Sun, and Y. Tay. "Deep learning based recommender system: A survey and new perspectives." ACM computing surveys (CSUR) 52, no. 1 (2019): 1-38. [11] V.R. Yannam, J. Kumar, K.S. Babu, and B. Sahoo. "Improving group recommendation using deep collaborative filtering approach." International Journal of Information Technology 15, no. 3 (2023): 1489-1497. [12] A. Delic, J. Neidhardt, T.N. Nguyen, and F. Ricci. "An observational user study for group recommender systems in the tourism domain." Information Technology & Tourism 19 (2018): 87-116. [13] A. Delic, J. Neidhardt, T.N. Nguyen, and F. Ricci. "Research Methods for Group Recommender System." In RecTour@ RecSys, pp. 30-37. 2016. [14] J. Masthoff. "Group recommender systems: aggregation, satisfaction and group attributes." recommender systems handbook (2015): 743-776. [15] L. Xiao, Z. Min, Z. Yongfeng, G. Zhaoquan, L. Yiqun, and M. Shaoping. "Fairness-aware group recommendation with pareto-efficiency." In Proceedings of the eleventh ACM conference on recommender systems, pp. 107-115. 2017. [16] A. Jameson, and B. Smyth. "Recommendation to groups." In The adaptive web: methods and strategies of web personalization, pp. 596-627. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. [17] D. Sacharidis. "Top-n group recommendations with fairness." In Proceedings of the 34th ACM/SIGAPP symposium on applied computing, pp. 1663-1670. 2019. [18] S. Berkovsky, and J. Freyne. "Group-based recipe recommendations: analysis of data aggregation strategies." In Proceedings of the fourth ACM conference on Recommender systems, pp. 111-118. 2010. [19] V.R. Kagita, A.K. Pujari, and V. Padmanabhan. "Group recommender systems: A virtual user approach based on precedence mining." In AI 2013: Advances in Artificial Intelligence: 26th Australasian Joint Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings 26, pp. 434-440. Springer International Publishing, 2013. [20] J. Masthoff. "Group recommender systems: Combining individual models." In Recommender systems handbook, pp. 677-702. Boston, MA: Springer US, 2010. [21] A. Felfernig, L. Boratto, M. Stettinger, and M. Tkalčič, eds. Group recommender systems: an introduction. Cham: Springer, 2024. [22] J. Masthoff. "Group modeling: Selecting a sequence of television items to suit a group of viewers." Personalized Digital Television: Targeting Programs to individual Viewers (2004): 93-141. [23] C. Senot, D. Kostadinov, M. Bouzid, J. Picault, and A. Aghasaryan. "Evaluation of group profiling strategies." In Twenty-Second International Joint Conference on Artificial Intelligence. 2011. [24] F. Brandt, V. Conitzer, and U. Endriss. "Computational social choice." Multiagent systems 2 (2012): 213-284. [25] M. Stettinger. "Choicla: Towards domain-independent decision support for groups of users." In Proceedings of the 8th ACM Conference on Recommender systems, pp. 425-428. 2014. [26] L. Xiao, and G. Zhaoquan. "How does fairness matter in group recommendation." In Web Information Systems Engineering–WISE 2017: 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part II 18, pp. 458-466. Springer International Publishing, 2017. [27] M. O’connor, D. Cosley, J.A. Konstan, and J. Riedl. "PolyLens: A recommender system for groups of users." In ECSCW 2001: Proceedings of the Seventh European conference on computer supported cooperative work 16–20 September 2001, Bonn, Germany, pp. 199-218. Springer Netherlands, 2001. [28] S. Ben Abdrabbah, M. Ayadi, R. Ayachi, and N. Ben Amor. "Aggregating top-k lists in group recommendation using borda rule." In Advances in Artificial Intelligence: From Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Arras, France, June 27-30, 2017, Proceedings, Part I 30, pp. 325-334. Springer International Publishing, 2017. [29] Y. Xiao, Q. Pei, L. Yao, S. Yu, L. Bai, and X. Wang. "An enhanced probabilistic fairness-aware group recommendation by incorporating social activeness." Journal of Network and Computer Applications 156 (2020): 102579. [30] D. Serbos, S. Qi, N. Mamoulis, E. Pitoura, and P. Tsaparas. "Fairness in package-to-group recommendations." In Proceedings of the 26th international conference on world wide web, pp. 371-379. 2017. [31] R.B. Nozari, and H. Koohi. "A novel group recommender system based on members’ influence and leader impact." Knowledge-Based Systems 205 (2020): 106296. [32] E. Yalcin, Fi. Ismailoglu, and A. Bilge. "An entropy empowered hybridized aggregation technique for group recommender systems." Expert Systems with Applications 166 (2021): 114111. [33] A. Pujahari, and D.S. Sisodia. "Preference relation based collaborative filtering with graph aggregation for group recommender system." Applied Intelligence 51 (2021): 658-672. [34] W.Wang, G. Zhang, and J. Lu. "Member contribution-based group recommender system." Decision Support Systems 87 (2016): 80-93. [35] A.F. Da Costa , M.G. Manzato, and R.J. Campello. "Group-based collaborative filtering supported by multiple users' feedback to improve personalized ranking." In Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web, pp. 279-286. 2016. [36] R. Abolghasemi, P. Engelstad, E. Herrera-Viedma, and A. Yazidi. "A personality-aware group recommendation system based on pairwise preferences." Information Sciences 595 (2022): 1-17. [37] B. Walek, and P. Fajmon. "A hybrid recommender system for an online store using a fuzzy expert system." Expert Systems with Applications 212 (2023): 118565. [38] V.R. Yannam, J. Kumar, K.S. Babu, and B.K. Patra. "Enhancing the accuracy of group recommendation using slope one." The journal of supercomputing 79, no. 1 (2023): 499-540. [39] J.a Kumar, V.R. Yannam, H. Prajapati, B. Sahoo, and K.S. Babu. "Improve the recommendation using hybrid tendency and user trust." International Journal of Information Technology 15, no. 6 (2023): 3147-3156.. [40] Y. Koren, R. Bell, and C. Volinsky. "Matrix factorization techniques for recommender systems." Computer 42, no. 8 (2009): 30-37. | ||
|
آمار تعداد مشاهده مقاله: 261 تعداد دریافت فایل اصل مقاله: 315 |
||