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Ahn HJ (2008) A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf Sci 178(1):37–51
Alashkar T, Jiang S, Wang S, Fu Y (2017) Examples-rules guided deep neural network for makeup recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol 31
Bilgic M, Mooney RJ (2005) Explaining recommendations: satisfaction versus promotion. In: Beyond personalization workshop, IUI, vol 5, p 153
Bobadilla J, González-Prieto Á, Ortega F, Lara-Cabrera R (2020) Deep learning feature selection to unhide demographic recommender systems factors. Neural Comput Appl 33:1–18
Bobadilla J, Hernando A, Ortega F, Bernal J (2011) A framework for collaborative filtering recommender systems. Expert Syst Appl 38(12):14609–14623
Bobadilla J, Ortega F, Gutiérrez A, Alonso S (2020) Classification-based deep neural network architecture for collaborative filtering recommender systems. IJIMAI 6(1):68–77. https://doi.org/10.9781/ijimai.2020.02.006
Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132
Bobadilla J, Serradilla F, Bernal J (2010) A new collaborative filtering metric that improves the behavior of recommender systems. Knowl Based Syst 23(6):520–528
Chollet F et al (2015) Keras. https://keras.io
Chollet F et al (2018) Deep learning with python, vol 361. Manning, New York
Dara S, Chowdary CR, Kumar C (2020) A survey on group recommender systems. J Intell Inf Syst 54(2):271–295
Dziugaite GK, Roy DM (2015) Neural network matrix factorization. arXiv:1511.06443
Ebesu T, Fang Y (2017) Neural citation network for context-aware citation recommendation. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 1093–1096
Feng C, Liang J, Song P, Wang Z (2020) A fusion collaborative filtering method for sparse data in recommender systems. Inf Sci 521:365–379
Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, NV, USA, June 27–30, 2016, pp 2414–2423. IEEE Computer Society. https://doi.org/10.1109/CVPR.2016.265
Gomez-Uribe CA, Hunt N (2015) The Netflix recommender system: algorithms, business value, and innovation. ACM Trans Manag Inf Syst (TMIS) 6(4):1–19
Gunes I, Kaleli C, Bilge A, Polat H (2014) Shilling attacks against recommender systems: a comprehensive survey. Artif Intell Rev 42(4):767–799
Guo G, Zhang J, Yorke-Smith N (2013) A novel bayesian similarity measure for recommender systems. In: Proceedings of the 23rd international joint conference on artificial intelligence (IJCAI), pp 2619–2625
Guo H, Tang R, Ye Y, Li Z, He X (2017) DeepFM: a factorization-machine based neural network for CTR prediction. arXiv:1703.04247
Harper FM, Konstan JA (2015) The movielens datasets: history and context. ACM Trans Interact Intell Syst 5(4):1–19
He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173–182
Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53
Logesh R, Subramaniyaswamy V, Malathi D, Sivaramakrishnan N, Vijayakumar V (2020) Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Comput Appl 32(7):2141–2164
Logesh R, Subramaniyaswamy V, Vijayakumar V, Gao XZ, Wang GG (2020) Hybrid bio-inspired user clustering for the generation of diversified recommendations. Neural Comput Appl 32(7):2487–2506
Misztal-Radecka J, Indurkhya B (2020) Getting to know your neighbors (KYN). Explaining item similarity in nearest neighbors collaborative filtering recommendations. In: Adjunct publication of the 28th ACM conference on user modeling, adaptation and personalization, pp 59–64
Mnih A, Salakhutdinov RR (2008) Probabilistic matrix factorization. In: Advances in neural information processing systems, pp 1257–1264
Moradi P, Ahmadian S (2015) A reliability-based recommendation method to improve trust-aware recommender systems. Expert Syst Appl 42(21):7386–7398
MyAnimeList.net: MyAnimeList dataset. https://www.kaggle.com/azathoth42/myanimelist (2020). Accessed 18 May 2020
Ortega F, Lara-Cabrera R, González-Prieto Á, Bobadilla J (2021) Providing reliability in recommender systems through Bernoulli matrix factorization. Inf Sci 553:110–128
Ortega F, Zhu B, Bobadilla J, Hernando A (2018) CF4J: Collaborative filtering for Java. Knowl Based Syst 152:94–99
Pádua FL, Lacerda A, Machado AC, Dalip DH et al (2019) Multimodal data fusion framework based on autoencoders for top-n recommender systems. Appl Intell 49(9):3267–3282
Portugal I, Alencar P, Cowan D (2018) The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst Appl 97:205–227
Raza S, Ding C (2019) Progress in context-aware recommender systems—an overview. Comput Sci Rev 31:84–97
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV) pp 618–626. https://doi.org/10.1109/ICCV.2017.74
Tahmasebi H, Ravanmehr R, Mohamadrezaei R (2020) Social movie recommender system based on deep autoencoder network using twitter data. Neural Comput Appl 33:1–17
Tang J, Wang K (2018) Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 565–573
Tuan TX, Phuong TM (2017)3D convolutional networks for session-based recommendation with content features. In: Proceedings of the eleventh ACM conference on recommender systems, pp 138–146
Turk AM, Bilge A (2019) Robustness analysis of multi-criteria collaborative filtering algorithms against shilling attacks. Expert Syst Appl 115:386–402
Wang D, Liang Y, Xu D, Feng X, Guan R (2018) A content-based recommender system for computer science publications. Knowl Based Syst 157:1–9
Yang X, Guo Y, Liu Y, Steck H (2014) A survey of collaborative filtering based social recommender systems. Comput Commun 41:1–10
Yi B, Shen X, Zhang Z, Shu J, Liu H: Expanded autoencoder recommendation framework and its application in movie recommendation. In: 2016 10th international conference on software, knowledge, information management and applications (SKIMA). IEEE, pp 298–303 (2016)
Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv (CSUR) 52(1):1–38
Zhang S, Yao L, Xu X (2017)Autosvd++ an efficient hybrid collaborative filtering model via contractive auto-encoders. In: Proceedings of the 40th international ACM SIGIR conference on Research and development in information retrieval, pp 957–960