Artificial intelligence in recommender systems

Complex & Intelligent Systems - Tập 7 Số 1 - Trang 439-457 - 2021
Qian Zhang1, Jie Lü1, Yaochu Jin2
1Decision Systems and e-Service Intelligence Laboratory, Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, 2007, Australia
2Department of Computer Science, University of Surrey, Guildford, Surrey, GU27XH, UK

Tóm tắt

Abstract

Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

Từ khóa


Tài liệu tham khảo

Shapira B, Ricci F, Kantor PB, Rokach L (2011) Recommender systems handbook. Springer, New York

Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132

Ben Schafer J, Konstan J, Riedl J (1999) Recommender systems in e-commerce. In: Proceedings of the 1st ACM Conference on Electronic Commerce, 1999, pp 158–166

Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32

Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-adapt Interact 12(4):331–370

Shardanand U, Maes P (1995) Social information filtering: algorithms for automating ‘word of mouth’. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1995, pp 210–217

Salton G, Wong A, Yang C-S (1975) A vector space model for automatic indexing. Commun ACM 18(11):613–620

Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1):1–47

Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53

Lops P, De Gemmis M, Semeraro G (2011) Content-based recommender systems: state of the art and trends. Recommender systems handbook. Springer, Berlin, pp 73–105

Shambour Q, Lu J (2012) A trust-semantic fusion-based recommendation approach for e-business applications. Decis Support Syst 54(1):768–780

Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72

Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, 1994, pp 175–186

Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80

Liu H, Hu Z, Mian A, Tian H, Zhu X (2014) A new user similarity model to improve the accuracy of collaborative filtering. Knowl Based Syst 56:156–166

Hu Y, Zhang D, Ye J, Li X, He X (2013) Fast and accurate matrix completion via truncated nuclear norm regularization. IEEE Trans Pattern Anal Mach Intell 35(9):2117–2130

Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:1–19

Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst 22(1):143–177

Shi Y, Larson M, Hanjalic A (2014) Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput Surv 47(1):3

Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

Luo X, Zhou M, Li S, You Z, Xia Y, Zhu Q (2016) A nonnegative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method. IEEE Trans Neural Netw Learn Syst 27(3):579–592

Liu B, Xiong H, Papadimitriou S, Fu Y, Yao Z (2015) A general geographical probabilistic factor model for point of interest recommendation. IEEE Trans Knowl Data Eng 27(5):1167–1179

Smyth B (2007) Case-based recommendation. The adaptive web. Springer, Berlin, pp 342–376

Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59

Felfernig A, Friedrich G, Jannach D, Zanker M (2011) Developing constraint-based recommenders. Recommender systems handbook. Springer, Berlin, pp 187–215

Felfernig A, Burke R (2008) Constraint-based recommender systems: technologies and research issues. In: Proceedings of the 10th International Conference on Electronic Commerce, 2008, p 3

Luger GF (2005) Artificial intelligence: structures and strategies for complex problem solving. Pearson Education, London

Russell SJ, Norvig P (2016) Artificial intelligence: a modern approach. Pearson Education Limited, Malaysia

LeCun Y, Bengio Y (1995) Convolutional networks for images, speech, and time series. Handb Brain Theor Neural Netw 3361(10):1995

Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press, Cambridge

Murtagh F (1991) Multilayer perceptrons for classification and regression. Neurocomputing 2(5–6):183–197

Wang Y, Yao H, Zhao S (2016) Auto-encoder based dimensionality reduction. Neurocomputing 184:232–242

Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98–113

Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536

Hochreiter S, Schmidhuber J (1997) LSTM can solve hard long time lag problems. Advances in neural information processing systems. MIT Press, Cambridge, pp 473–479

Goodfellow I et al (2014) Generative adversarial nets. Advances in neural information processing systems. MIT Press, Cambridge, pp 2672–2680

Zhou J et al (2018) Graph neural networks: a review of methods and applications. arXiv Prepr. arXiv1812.08434

Lu J, Zuo H, Zhang G (2019) Fuzzy multiple-source transfer learning. IEEE Trans. Fuzzy Syst

Lu J, Behbood V, Hao P, Zuo H, Xue S, Zhang G (2015) Transfer learning using computational intelligence: a survey. Knowl Based Syst 80:14–23

Kang Z, Grauman K, Sha F (2011) Learning with whom to share in multi-task feature learning. In: The 28th International Conference on Machine Learning, pp 521–528

Arnold A, Nallapati R, Cohen WW (2007) A comparative study of methods for transductive transfer learning. In: The 7th IEEE International Conference on Data Mining Workshops, 2007, pp 77–82

Lu J, Xuan J, Zhang G, Luo X (2018) Structural property-aware multilayer network embedding for latent factor analysis. Pattern Recogn 76:228–241

Zhu X, Lafferty J, Rosenfeld R (2005) Semi-supervised learning with graphs. Carnegie Mellon University, Language Technologies Institute, School of Computer Science, Pittsburgh

Aghdam HH, Gonzalez-Garcia A, van de Weijer J, López AM (2019) Active learning for deep detection neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, 2019, pp 3672–3680

Settles B (2011) From theories to queries: active learning in practice. In: Active Learning and Experimental Design workshop in conjunction with AISTATS 2010, 2011, pp 1–18

Settles B (2010) Active learning literature survey. University of California, Santa Cruz

Sutton RS, Barto AG (2011) Reinforcement learning: An introduction. MIT Press, Cambridge

Peng et al P (2017) Multiagent bidirectionally-coordinated nets: emergence of human-level coordination in learning to play starcraft combat games. arXiv Prepr. arXiv1703.10069

Bai W, Li T, Tong S (2020) NN reinforcement learning adaptive control for a class of nonstrict-feedback discrete-time systems. IEEE Trans Cybern

Hüttenrauch M, Adrian S, Neumann G (2019) Deep reinforcement learning for swarm systems. J Mach Learn Res 20(54):1–31

Neftci EO, Averbeck BB (2019) Reinforcement learning in artificial and biological systems. Nat Mach Intell 1(3):133–143

Botvinick M, Ritter S, Wang JX, Kurth-Nelson Z, Blundell C, Hassabis D (2019) Reinforcement learning, fast and slow. Trends Cogn Sci 23(5):408–422

Bellman R (1957) A Markovian decision process. J Math Mech 679–684

Henderson P, Islam R, Bachman P, Pineau J, Precup D, Meger D (2017) Deep reinforcement learning that matters. arXiv Prepr. arXiv1709.06560

Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285

Mnih V et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533

Silver D et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489

Tran L, Duckstein L (2002) Comparison of fuzzy numbers using a fuzzy distance measure. Fuzzy Sets Syst 130(3):331–341

Roubos JA, Setnes M, Abonyi J (2003) Learning fuzzy classification rules from labeled data. Inf Sci (Ny) 150(1–2):77–93

Chen S-M, Wang C-Y (2013) Fuzzy decision making systems based on interval type-2 fuzzy sets. Inf Sci (Ny) 242:1–21

Holland JH (1975) Adaption in natural and artificial systems

Beyer H-G, Beyer H-G, Schwefel H-P, Schwefel H-P (2002) Evolution strategies: a comprehensive introduction. Nat Comput 1(1):3–52

Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. The MIT Press, Cambridge

Larrañaga P, Lozano JA (2001) Estimation of distribution algorithms: a new tool for evolutionary computation, vol 2. Springer Science & Business Media, Berlin

Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, 1995, pp 39–43

Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

Miettinen K (1999) Nonlinear multiobjective optimization. Kluwer Academic Publishers, Dordrecht

Li B, Li J, Tang K, Yao X (2015) Many-objective evolutionary algorithms: a survey. ACM Comput Surv 48(1):1–35

Chowdhary KR (2020) Natural language processing. Fundamentals of artificial intelligence. Springer, Berlin, pp 603–649

Chien J-T (2019) Deep Bayesian natural language processing. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, 2019, pp 25–30

Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493–2537

Zhang W, Yoshida T, Tang X (2008) Text classification based on multi-word with support vector machine. Knowl Based Syst 21(8):879–886

Yi J, Nasukawa T, Bunescu R, Niblack W (2003) Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In: Third IEEE international conference on data mining, 2003, pp 427–434

Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J. Mach. Learn. Res. 3:993–1022

Forsyth DA, Ponce J (2002) Computer vision: a modern approach. Prentice Hall Professional Technical Reference, Upper Saddle River

Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci 2018:7068349

Khan S, Rahmani H, Shah SAA, Bennamoun M (2018) A guide to convolutional neural networks for computer vision. Synth Lect Comput Vis 8(1):1–207

Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, 2007, pp 791–798

Truyen TT, Phung DQ, Venkatesh S (2009) Ordinal Boltzmann machines for collaborative filtering. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2009, pp 548–556

Zhang S, Yao L (2017) Deep learning based recommender system: a survey and new perspectives. ACM J Comput Cult Herit Artic 1(35):1–35

Cheng et al. HT (2016) Wide and deep learning for recommender systems. arXiv Prepr. pp 1–4

Guo H, Tang R, Ye Y, Li Z, He X (2017) DeepFM: a factorization-machine based neural network for CTR prediction. In: International Joint Conference on Artificial Intelligence, 2017, pp 1725–1731

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, 2017, pp 173–182

Sedhain S, Menon AK, Sanner S, Xie L (2015) AutoRec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web, 2015, pp 111–112

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, 2017, pp 2–5

Strub F, Gaudel R, Mary J (2016) Hybrid recommender system based on autoencoders. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 2016, pp 1–5

Diao Q, Qiu M, Wu CY, Smola AJ, Jiang J, Wang C (2014) Jointly modeling aspects, ratings and sentiments for movie recommendation. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, pp 193–202

Kim D, Park C, Oh J, Lee S, Yu H (2016) Convolutional matrix factorization for document context-aware recommendation. In: RecSys 2016—Proceedings of the 10th ACM Conference on Recommender Systems, 2016, pp 233–240

Yuyun G, Qi Z (2016) Hashtag recommendation using attention-based convolutional neural network. In: International Joint Conference on Artificial Intelligence, 2016, pp 2782–2788

Dai H, Wang Y, Trivedi R, Song L (2016) Recurrent coevolutionary feature embedding processes for recommendation. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 2016, pp 1–11

Wu CY, Ahmed A, Beutel A, Smola AJ, Jing H (2017) Recurrent recommender networks. In: Proceedings of the 10th ACM International Conference on Web Search and Data Mining, 2017, pp 495–503

Jing H, Smola AJ (2017) Neural survival recommender. In: Proceedings of the 10th ACM International Conference on Web Search and Data Mining, 2017, pp 515–524

Wang S, Hu L, Wang Y, Cao L, Sheng QZ, Orgun M (2019) Sequential recommender systems: challenges, progress and prospects. arXiv Prepr. arXiv2001.04830

Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks. In: 4th Int. Conf. Learn. Represent, pp 1–10, 2016

Wu S, Ren W, Yu C, Chen G, Zhang D, Zhu J (2016) Personal recommendation using deep recurrent neural networks in NetEase. In: Proceeding of the 32nd International Conference on Data Engineering, 2016, pp 1218–1229

Li J, Ren R, Chen Z, Ren Z, Lian T, Ma J (2017) Neural attentive session-based recommendation. In: Int. Conf. Inf. Knowl. Manag. Proc., vol. Part F1318, pp 1419–1428, 2017

Liu Q, Zeng Y, Mokhosi R, Zhang H (2018) STAMP: Short-term attention/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018, pp. 1831–1839

Ying H et al. (2018) Sequential recommender system based on hierarchical attention network. In: International Joint Conference on Artificial Intelligence, 2018.

Wang J et al. (2017) IRGAN: a minimax game for unifying generative and discriminative information retrieval models. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017, pp 515–524

He X, He Z, Du X, Chua TS (2018) Adversarial personalized ranking for recommendation. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018, pp 355–364

Yang D, Guo Z, Wang Z, Jiang J, Xiao Y, Wang W (2018) A knowledge-enhanced deep recommendation framework incorporating GAN-based models. In: 2018 IEEE International Conference on Data Mining, 2018, pp 1368–1373

Tang J, Du X, He X, Yuan F, Tian Q, Chua T-S (2019) Adversarial training towards robust multimedia recommender system. IEEE Trans Knowl Data Eng 32(5):855–867

Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018, pp 974–983

Yin R, Li K, Zhang G, Lu J (2019) A deeper graph neural network for recommender systems. Knowl Based Syst 185:105020

Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T (2019) Session-based recommendation with graph neural Networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2019, vol. 33, pp 346–353

Cantador I, Fernández-Tobías I, Berkovsky S, Cremonesi P (2015) Cross-domain recommender systems. Recommender systems handbook. Springer, Berlin, pp 919–959

Singh AP, Gordon GJ (2008) Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008, pp 650–658

Yang D, He J, Qin H, Xiao Y, Wang W (2015) A graph-based recommendation across heterogeneous domains, In

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015, pp 463-472

Abel F, Herder E, Houben G-J, Henze N, Krause D (2013) Cross-system user modeling and personalization on the social web. User Model. User-adapt. Interact, pp 1–41

Zhen Y, Li WJ, Yeung DY (2009) TagiCoFi: Tag informed collaborative filtering. In: RecSys’09—Proceedings of the 3rd ACM Conference on Recommender Systems, 2009, pp 69–76

Hao P, Zhang G, Martinez L, Lu J (2017) Regularizing knowledge transfer in recommendation with tag-inferred correlation. IEEE Trans Cybern

Li B, Yang Q, Xue X (2009) Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, 2009, vol 9, pp 2052–2057

Li B, Yang Q, Xue X (2009) Transfer learning for collaborative filtering via a rating-matrix generative model. In: Proceedings of the 26th International Conference on Machine Learning, ICML 2009, 2009, pp 617–624

Zhang Q, Wu D, Lu J, Liu F, Zhang G (2017) A cross-domain recommender system with consistent information transfer. Decis Support Syst 104:49–63

Zhang Y, Cao B, Yeung DY (2010) Multi-domain collaborative filtering. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, 2010, pp 725–732

Pan W, Yang Q (2013) Transfer learning in heterogeneous collaborative filtering domains. Artif Intell 197:39–55

Hu L, Cao J, Xu G, Cao L, Gu Z, Zhu C (2013) Personalized recommendation via cross-domain triadic factorization. In: Proceedings of the 22nd International Conference on World Wide Web, 2013, pp 595–606

Mirbakhsh N, Ling CX (2015) Improving top-n recommendation for cold-start users via cross-domain information. ACM Trans Knowl Discov Data 9(4):33

Li CY, Lin SD (2014) Matching users and items across domains to improve the recommendation quality. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, pp 801–810

Zhao L, Pan SJ, Yang Q (2017) A unified framework of active transfer learning for cross-system recommendation. Artif Intell 245:38–55

Zhang Q, Lu J, Wu D, Zhang G (2019) A cross-domain recommender system with kernel-induced knowledge transfer for overlapping entities. IEEE Trans Neural Netw Learn Syst 30(7):1998–2012

Zhu F, Wang Y, Chen, Liu G, Orgun M, Wu (2018) A deep framework for cross-domain and cross-system recommendations. In: IJCAI International Joint Conference on Artificial Intelligence, 2018

Hu G, Zhang Y, Yang Q (2018) Conet: collaborative cross networks for cross-domain recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018, pp 667–676

Wang C, Niepert M, Li H (2019) Recsys-dan: discriminative adversarial networks for cross-domain recommender systems. IEEE Trans Neural Networks Learn Syst

Yuan F, Yao L, Benatallah B (2019) DARec: Deep domain adaptation for cross-domain recommendation via transferring rating patterns. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Zhu F, Chen C, Wang Y, Liu G, Zheng X (2019) Dtcdr: a framework for dual-target cross-domain recommendation. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019, pp 1533–1542

Elahi M, Ricci F, Rubens N (2016) A survey of active learning in collaborative filtering recommender systems. Comput Sci Rev 20:29–50

Boutilier C, Zemel RS, Marlin B (2003) Active collaborative filtering. In: Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence, 2003, pp 98–106

Mello CE, Aufaure MA, Zimbrao G (2010) Active learning driven by rating impact analysis. In: Proceedings of the 4th ACM Conference on Recommender Systems, 2010, pp 341–344

Golbandi N, Koren Y, Lempel R (2010) On bootstrapping recommender systems. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, 2010, pp 1805–1808

Karimi R, Freudenthaler C, Nanopoulos A, Schmidt-Thieme L (2011) Active learning for aspect model in recommender systems. In: IEEE Symposium on Computational Intelligence and Data Mining, 2011, pp 162–167

Golbandi N, Koren Y, Lempel R (2011) Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining, 2011, pp 595–604

Karimi R, Freudenthaler C, Nanopoulos A, Schmidt-Thieme L (2011) Non-myopic active learning for recommender systems based on matrix factorization. In: IEEE International Conference on Information Reuse & Integration, 2011, pp 299–303

Wiesner M, Pfeifer D (2010) Adapting recommender systems to the requirements of personal health record systems. In: Proceedings of the 1st ACM International Health Informatics Symposium, 2010, pp 410–414

Elahi M, Ricci F, Rubens N (2012) Adapting to natural rating acquisition with combined active learning strategies. In: International Symposium on Methodologies for Intelligent Systems, 2012, pp 254–263

Rubens N, Sugiyama M (2007) Influence-based collaborative active learning. In: Proceedings of the 1st ACM Conference on Recommender Systems, 2007, pp 145–148

He L, Liu NN, Yang Q (2011) Active dual collaborative filtering with both item and attribute feedback. In: Proceedings of the National Conference on Artificial Intelligence, 2011, vol. 2, pp 1186–1191

Zhang Z, Jin X, Li L, Ding G, Yang Q (2016) Multi-domain active learning for recommendation. In: AAAI, 2016, pp 2358–2364

Berry DA, Fristedt B (1985) Bandit problems: sequential allocation of experiments (Monographs on statistics and applied probability). London Chapman Hall 5(71–87):7

Shani G, Heckerman D, Brafman RI (2005) An MDP-based recommender system. J Mach Learn Res 6:1265–1295

Warlop R et al (2018) Fighting boredom in recommender systems with linear reinforcement learning. No. NeurIPS, 2018

Wang H, Wu Q, Wang H (2017) Factorization bandits for interactive recommendation. AAAI 17:2695–2702

Li L, Chu W, Langford J, Schapire RE (2010) A contextual-bandit approach to personalized news article recommendation. In: Proc. 19th Int. Conf. World Wide Web, pp. 661–670, 2010

Zeng C, Wang Q, Mokhtari S, Li T (2016) Online context-aware recommendation with time varying multi-armed bandit. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp 2025–2034

Zheng G et al (2018) DRN: a deep reinforcement learning framework for news recommendation. Proc World Wide Web Conf 2:167–176

Zhao X, Xia L, Zhang L, Ding Z, Yin D, Tang J (2018) Deep reinforcement learning for page-wise recommendations. In: 12th ACM Conf Recomm Syst, pp 95–103, 2018

Zhao X, Xia L, Zhang L, Tang J, Ding Z, Yin D (2018) Recommendations with negative feedback via pairwise deep reinforcement learning. In: Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 1040–1048, 2018

Zhou S et al (2020) Interactive recommender system via knowledge graph-enhanced reinforcement learning. pp 179–188

Ie E et al (2019) SLateq: a tractable decomposition for reinforcement learning with recommendation sets. In: Int Jt Conf Artif Intell, vol. 2019-Augus, pp 2592–2599, 2019

Hu Y, Da Q, Zeng A, Yu Y, Xu Y (2018) Reinforcement learning to rank in E-commerce search engine: Formalization, analysis, and application. In: Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp 368–377, 2018

Chung F, Rhee H (2007) “Uncertain fuzzy clustering: insights and recommendations. IEEE Comput Intell Mag 2(1):44–56

Yager RR (2003) Fuzzy logic methods in recommender systems. Fuzzy Sets Syst 136(2):133–149

Zenebe A, Zhou L, Norcio AF (2010) User preferences discovery using fuzzy models. Fuzzy Sets Syst 161(23):3044–3063

Mao M, Lu J, Zhang G, Zhang J (2015) A fuzzy content matching-based e-commerce recommendation approach. In: IEEE International Conference on Fuzzy Systems, 2015

Wu D, Zhang G, Lu J (2015) A fuzzy preference tree-based recommender system for personalized business-to-business e-services. IEEE Trans Fuzzy Syst 23(1):29–43

Zhang Z, Lin H, Liu K, Wu D, Zhang G, Lu J (2013) A hybrid fuzzy-based personalized recommender system for telecom products/services. Inf Sci (Ny) 235:117–129

Yera R, Castro J, Martínez L (2016) A fuzzy model for managing natural noise in recommender systems. Appl Soft Comput J 40:187–198

Cornelis C, Lu J, Guo X, Zhang G (2007) One-and-only item recommendation with fuzzy logic techniques. Inf Sci (Ny) 177(22):4906–4921

Son LH, Thong NT (2015) Intuitionistic fuzzy recommender systems: an effective tool for medical diagnosis. Knowl Based Syst 74:133–150

Zhang Q, Wu D, Zhang G, Lu J (2016) Fuzzy user-interest drift detection based recommender systems. In: International Conference on Fuzzy Systems, 2016, pp 1274–1281

Nilashi M, Bin-Ibrahim O, Ithnin N (2014) “Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and neuro-fuzzy system. Knowl Based Syst 60:82–101

Nilashi M, Bin-Ibrahim O, Ithnin N (2014) Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Syst Appl 41(8):3879–3900

Treerattanapitak K, Jaruskulchai C (2012) Exponential fuzzy C-means for collaborative filtering. J Comput Sci Technol 27(3):567–576

Xu S, Watada J (2014) A method for hybrid personalized recommender based on clustering of fuzzy user profiles. In: IEEE International Conference on Fuzzy Systems, 2014, pp 2171–2177

Kant V, Bharadwaj KK (2013) Integrating collaborative and reclusive methods for effective recommendations: a fuzzy Bayesian approach. Int J Intell Syst 28(11):1099–1123

de Campos LM, Fernández-Luna JM, Huete JF (2008) A collaborative recommender system based on probabilistic inference from fuzzy observations. Fuzzy Sets Syst 159(12):1554–1576

Serrano-Guerrero J, Herrera-Viedma E, Olivas JA, Cerezo A, Romero FP (2011) A Google wave-based fuzzy recommender system to disseminate information in University Digital Libraries 2.0. Inf Sci (Ny) 181(9):1503–1516

Bedi P, Vashisth P (2014) Empowering recommender systems using trust and argumentation. Inf Sci (Ny) 279(22):569–586

Zhang X, Duan F, Zhang L, Cheng F, Jin Y, Tang K (2017) Pattern recommendation in task-oriented applications: a multi-objective perspective. IEEE Computational Intelligence Magazine, vol. 12, no. 3, IEEE, pp 43–53, 2017

Ribeiro MT, Lacerda A, Veloso A, Ziviani N (2012) Pareto-efficient hybridization for multi-objective recommender systems. In: Proceedings of the 6th ACM Conference on Recommender Systems, 2012, pp 19–26

Rodriguez M, Posse C, Zhang E (2012) Multiple objective optimization in recommender systems. In: Proceedings of the 6th ACM Conference on Recommender Systems, 2012, pp 11–18

Karabadji NEI, Beldjoudi S, Seridi H, Aridhi S, Dhifli W (2018) Improving memory-based user collaborative filtering with evolutionary multi-objective optimization. Expert Syst Appl 98:153–165

Mu C, Jiao L, Liu Y, Li Y (2015) Multiobjective nondominated neighbor coevolutionary algorithm with elite population. Soft Comput 19(5):1329–1349

Rana C, Jain SK (2015) A study of the dynamic features of recommender systems. Artif Intell Rev 43(1):141–153

Chen Y, Sun X, Gong D, Zhang Y, Choi J, Klasky S (2017) Personalized search inspired fast interactive estimation of distribution algorithm and its application. IEEE Trans Evol Comput 21(4):588–600

Adomavicius G, Kwon Y (2015) Multi-criteria recommender systems. Recommender systems handbook. Springer, Berlin, pp 847–880

Konečný J, McMahan HB, Yu FX, Richtárik P, Suresh AT, Bacon D (2016) Federated learning: strategies for improving communication efficiency. arXiv Prepr. arXiv1610.05492

Huang L, Joseph AD, Nelson B, Rubinstein BIP, Tygar JD (2011) Adversarial machine learning. In: Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, 2011, pp 43–58

Zhu H, Jin Y (2020) Multi-objective evolutionary federated learning. IEEE Trans Neural Netw Learn Syst 31(4):1310–1322

Zhang W, Ding G, Chen L, Li C, Zhang C (2013) Generating virtual ratings from chinese reviews to augment online recommendations. ACM Trans Intell Syst Technol 4(1):1–17

Agarwal D, Chen BC (2010) fLDA: matrix factorization through latent Dirichlet allocation. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, 2010, pp 91–100

Dong R, Schaal M, O’Mahony MP, McCarthy K, Smyth B (2013) Sentimental product recommendation. In: Proceedings of the 7th ACM Conference on Recommender Systems, 2013, pp 44–58

McAuley J, Leskovec J (2013) From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In: Proc. 22nd Int. Conf. World Wide Web, pp 897–908

McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM Conference on Recommender Systems, 2013, pp 165–172

Ling G, Lyu MR, King I (2014) Ratings meet reviews, a combined approach to recommend. In: Proceedings of the 8th ACM Conference on Recommender Systems, 2014, pp 105–112

Xin X, Liu Z, Lin CY, Huang H, Wei X, Guo P (2015) Cross-domain collaborative filtering with review text. In: International Joint Conference on Artificial Intelligence, 2015, pp 1827–1834

Barkan O, Noam K (2016) Item2vec: neural item embedding for CF. In: IEEE 26th International Workshop on Machine Learning for Signal Processing, 2016, pp 1–6

Sun Z, Yang J, Zhang J, Bozzon A, Chen Y, Xu C (2017) MRLR: multi-level representation learning for personalized ranking in recommendation. In: International Joint Conference on Artificial Intelligence, 2017, pp 2807–2813

Iovine A, Narducci F, Semeraro G (2020) Conversational recommender systems and natural language: a study through the ConveRSE framework. Decis Support Syst 131:113250

Lei C, Liu D, Li W, Zha ZJ, Li H (2016) Comparative deep learning of hybrid representations for image recommendations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp 2545–2553

He R, McAuley J (2015) VBPR: visual bayesian personalized ranking from implicit feedback. In: AAAI, 2015, pp 144–150

Gaspar P (2017) User preferences analysis using visual stimuli. In: Proceedings of the 11th ACM Conference on Recommender Systems, 2017, pp 436–440

Zhao L, Lu Z, Pan SJ, Yang Q (2016) Matrix factorization+ for movie recommendation. In: International Joint Conference on Artificial Intelligence, 2016, pp 3945–3951

Wang S, Wang Y, Tang J, Shu K, Ranganath S, Liu H (2017) What your images reveal: exploiting visual contents for point-of-interest recommendation. In: Proceedings of the 26th International Conference on World Wide Web, 2017, pp 391–400

He R, McAuley J (2016) Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web, 2016, pp 507–517

Jaradat S (2017) Deep cross-domain fashion recommendation. In: Proceedings of the 11th ACM Conference on Recommender Systems, 2017, pp 407–410

Lu J, Liu A, Song Y, Zhang G (2020) Data-driven decision support under concept drift in streamed big data. Complex Intell Syst 6(1):157–163

Harries M, Horn K (1995) Detecting concept drift in financial time series prediction using symbolic machine learning. In: Proceedings of the 8th Australian Joint Conference on Artificial Intelligence, 1995, pp 91–98

Campos PG, Díez F, Cantador I (2014) Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model User-Adapt Interact 24(1–2):67–119

Yin H, Cui B, Chen L, Hu Z, Zhou X (2015) Dynamic user modeling in social media systems. ACM Trans Inf Syst 33(3):10

Chua FCT, Oentaryo RJ, Lim EP (2013) Modeling temporal adoptions using dynamic matrix factorization. In: Proceedings of IEEE International Conference on Data Mining, 2013, pp 91–100

Yin H, Cui B, Li J, Yao J, Chen C (2012) Challenging the long tail recommendation. In: Proceedings of the VLDB Endowment, 2012, vol 5, no 9, pp 896–907

Canny J (2002) Collaborative filtering with privacy. In: Proc. IEEE Symp. Secur. Priv., vol. 2002-Jan, pp 45–57, 2002

Kikuchi H, Mochizuki A (2013) Privacy-preserving collaborative filtering using randomized response. J Inf Process 21(4):617–623

Chow R, Pathak MA, Wang C (2012) A practical system for privacy-preserving collaborative filtering. In: Proc. 12th IEEE Int. Conf. Data Min. Work. ICDMW 2012, pp 547–554, 2012

Bostandjiev S, O’Donovan J, Höllerer T (2012) TasteWeights: a visual interactive hybrid recommender system. In: Proceedings of the sixth ACM conference on Recommender systems, 2012, pp 35–42

Wang W, Zhang G, Lu J (2017) Hierarchy visualization for group recommender systems. In: IEEE Trans Syst Man Cybern Syst, pp 1–12, 2017

Hernando A, Moya R, Ortega F, Bobadilla J (2014) Hierarchical graph maps for visualization of collaborative recommender systems. J Inf Sci 40(1):97–106