ترکیب تجزیه نامنفی ماتریسی با روابط اعتماد برای توصیه در شبکه‌های اجتماعی

[1]    T. D. T. Do and L. Cao, “Coupled Poisson factorization integrated with user/item metadata for modeling popular and sparse ratings in scalable recommendation,” Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), pp. 1-7, 2018.

[2]    X. Luo, M. Zhou, S. Li, Z. You, Y. Xia and Q. Zhu, “A nonnegative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, pp. 579-592, 2016.

[3]    B. Yang, Y. Lei, J. Liu and W. Li, “Social collaborative filtering by trust,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, pp. 1633-1647, 2017.

[4]    X. Luo, M. Zhou, Y. Xia and Q. Zhu, “An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems,” IEEE Transactions on Industrial Informatics, vol. 10, pp. 1273-1284, 2014.

[5]    A. Hernando, J. Bobadilla and F. Ortega, “A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model,” Knowledge-Based Systems, vol. 97, pp. 188-202, 2016.

[6]    H. Ma, I. King and M. R. Lyu, “Learning to recommend with social trust ensemble,” in Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Iinformation Retrieval, pp. 203-210, 2009.

[7]    D. Z. Navgaran, P. Moradi and F. Akhlaghian, “Evolutionary based matrix factorization method for collaborative filtering systems,” in Electrical Engineering (ICEE), 2013 21st Iranian Conference on, pp. 1-5, 2013.

[8]    H. Parvin, P. Moradi and S. Esmaeili, “Nonnegative matrix factorization regularized with trust relationships for solving cold-start problem in recommender Systems,” in Electrical Engineering (ICEE), Iranian Conference on, pp. 1571-1576, 2018.

[9]    M. Ranjbar, P. Moradi, M. Azami and M. Jalili, “An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems,” Engineering Applications of Artificial Intelligence, vol. 46, pp. 58-66, 2015.

[10]  P. Pirasteh, D. Hwang and J. J. Jung, “Exploiting matrix factorization to asymmetric user similarities in recommendation systems,” Knowledge-Based Systems, vol. 83, pp. 51-57, 2015.

]11[ فریاد طهماسبی, مجید مقدادی و سجاد احمدیان، «ارائه یک روش ترکیبی جدید بر اساس تکنیک گسترش پروفایل برای حل مسئله شروع سرد در سیستم‌های توصیه‌گر»، مجله مهندسی برق دانشگاه تبریز، جلد 48، صفحه 151-159، 1397.

[12]  J. Bobadilla, F. Ortega, A. Hernando and J. Bernal, “A collaborative filtering approach to mitigate the new user cold start problem,” Knowledge-Based Systems, vol. 26, pp. 225-238, 2012.

[13]  K. H. Tso-Sutter, L. B. Marinho and L. Schmidt-Thieme, “Tag-aware recommender systems by fusion of collaborative filtering algorithms,” in Proceedings of the 2008 ACM Symposium on Applied Computing,  pp. 1995-1999, 2008.

[14]  H. Parvin, P. Moradi and S. Esmaeili, “A collaborative filtering method based on genetic algorithm and trust statements,” in Fuzzy and Intelligent Systems (CFIS), 2018 6th Iranian Joint Congress on, pp. 13-16, 2018.

[15]  J. Bobadilla, R. Bojorque, A. H. Esteban and R. Hurtado, “Recommender Systems Clustering Using Bayesian Non Negative Matrix Factorization,” IEEE Access, vol. 6, pp. 3549-3564, 2018.

[16]  B. Sarwar, G. Karypis, J. Konstan and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th International Conference on World Wide Web, pp. 285-295, 2001.

[17]  G. Guo, J. Zhang and N. Yorke-Smith, “Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems,” Knowledge-Based Systems, vol. 74, pp. 14-27, 2015.

[18]  G. Guo, J. Zhang and N. Yorke-Smith, “TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings,” in Twnety-Ninth AAAI Conference on Artificial Intelligence (AAAI-15),  pp. 123-125, 2015.

[19]  M. Jamali and M. Ester, “A matrix factorization technique with trust propagation for recommendation in social networks,” Proceedings of the Fourth ACM Conference on Recommender Systems, Barcelona, Spain, 2010.

]20[ اکرم ناظمی سجزی و مرجان کائدی, «اصلاح نویز طبیعی در سیستم‌های توصیه‌گر مشارکتی با در نظر گرفتن تغییر ترجیحات کاربر»، مجله مهندسی برق دانشگاه تبریز، جلد 48، صفحه 345-356، 1397.

[21]  X. Luo, M. Zhou, H. Leung, Y. Xia, Q. Zhu, Z. You, et al., “An incremental-and-static-combined scheme for matrix-factorization-based collaborative filtering,” IEEE Transactions on Automation Science and Engineering, vol. 13, pp. 333-343, 2016.

[22]  A. Popescul, D. M. Pennock and S. Lawrence, “Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments,” in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pp. 437-444, 2001.

[23]  M. J. Pazzani and D. Billsus, “Content-based recommendation systems,” in The Adaptive Web: Methods and Strategies of Web Personalization, P. Brusilovsky, A. Kobsa, and W. Nejdl, Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg,  pp. 325-341, 2007.

[24]  I. Cantador, A. Bellogín and D. Vallet, “Content-based recommendation in social tagging systems,” in Proceedings of the fourth ACM Conference on Recommender Systems, pp. 237-240, 2010.

[25]  L. Yao, Q. Z. Sheng, A. H. H. Ngu, J. Yu and A. Segev, “Unified collaborative and content-based web service recommendation,” IEEE Transactions on Services Computing, vol. 8, pp. 453-466, 2015.

[26]  J. Wang, A. P. De Vries and M. J. Reinders, “Unifying user-based and item-based collaborative filtering approaches by similarity fusion,” in Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 501-508, 2006.

[27]  Y. Shi, M. Larson and A. Hanjalic, “List-wise learning to rank with matrix factorization for collaborative filtering,” Proceedings of the Fourth ACM Conference on Recommender Systems, Barcelona, Spain, 2010.

[28]  M. Gao, Z. Wu and F. Jiang, “Userrank for item-based collaborative filtering recommendation,” Information Processing Letters, vol. 111, pp. 440-446, 2011.

[29]  A. Hawalah and M. Fasli, “Utilizing contextual ontological user profiles for personalized recommendations,” Expert Systems with Applications, vol. 41, pp. 4777-4797, 2014.

[30]  H. Parvin, P. Moradi, S. Esmaeili and M. Jalili, “An efficient recommender system by integrating non-negative matrix factorization with trust and distrust relationships,” in 2018 IEEE Data Science Workshop (DSW), pp. 135-139, 2018.

[31]  H. Ma, H. Yang, M. R. Lyu and I. King, “Sorec: social recommendation using probabilistic matrix factorization,” in Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931-940, 2008.

[32]  G. Guo, J. Zhang and N. Yorke-Smith, “A novel recommendation model regularized with user trust and item ratings,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, pp. 1607-1620, 2016.

[33]  S. Boyd, “Alternating direction method of multipliers,” in Talk at NIPS Workshop on Optimization and Machine Learning, 2011.