Abstract
Since the first years of the 90s, recommender systems have emerged as effective tools for automatically selecting items according to user preferences. Traditional recommenders rely on the relevance assessments that users express using a single rating for each item. However, some authors started to suggest that this approach could be limited, as we naturally tend to formulate different judgments according to multiple criteria. During the last decade, several studies introduced novel recommender systems capable of exploiting user preferences expressed over multiple criteria. This work proposes a systematic literature review in the field of multicriteria recommender systems. Following a replicable protocol, we selected a total number of 93 studies dealing with this topic. We subsequently analyzed them to provide an answer to five different research questions. We considered what are the most common research problems, recommendation approaches, data mining and machine learning algorithms mentioned in these studies. Furthermore, we investigated the domains of application, the exploited evaluation protocols, metrics and datasets, and the most promising suggestions for future works.
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Notes
Please note that the results for the year 2019 are not available, as the selection was performed in January 2019.
References
Adomavicius G, Kwon Y (2015) Multi-criteria recommender systems. In: Recommender systems handbook. Springer, pp 847–880. https://doi.org/10.1007/978-1-4899-7637-6_25
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. https://doi.org/10.1109/tkde.2005.99
Adomavicius G, Tuzhilin A (2015) Context-aware recommender systems. In: Recommender systems handbook. Springer, pp 191–226 https://doi.org/10.1007/978-1-4899-7637-6_6
Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72. https://doi.org/10.1145/245108.245124
Burke R (2007) Hybrid web recommender systems. In: The adaptive web. Springer, Berlin, pp 377–408. https://doi.org/10.1007/978-3-540-72079-9_12
Çano E, Morisio M (2017) Hybrid recommender systems: a systematic literature review. Intell Data Anal 21(6):1487–1524. https://doi.org/10.3233/IDA-163209
Cruzes DS, Dyba T (2011) Recommended steps for thematic synthesis in software engineering. In: 2011 international symposium on empirical software engineering and measurement. IEEE. https://doi.org/10.1109/esem.2011.36
de Gemmis M, Lops P, Musto C, Narducci F, Semeraro G (2015) Semantics-aware content-based recommender systems. In: Recommender systems handbook. Springer, pp 119–159. https://doi.org/10.1007/978-1-4899-7637-6_4
Figueroa C, Vagliano I, Rocha OR, Morisio M (2015) A systematic literature review of linked data-based recommender systems. Concurr Comput Pract Exp 27(17):4659–4684. https://doi.org/10.1002/cpe.3449
Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70. https://doi.org/10.1145/138859.138867
Gunawardana A, Shani G (2015) Evaluating recommender systems. In: Recommender systems handbook, 2 edn, chap. 8. Springer, pp 265–308. https://doi.org/10.1007/978-1-4899-7637-6_8
Guy I (2015) Social recommender systems. In: Recommender systems handbook. Springer, pp 511–543. https://doi.org/10.1007/978-1-4899-7637-6_15
Herlocker JL, Konstan JA, Riedl J (2000). Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM conference on computer supported cooperative work. ACM https://doi.org/10.1145/358916.358995
Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53. https://doi.org/10.1145/963770.963772
Hong J, Suh E, Kim SJ (2009) Context-aware systems: a literature review and classification. Expert Syst Appl 36(4):8509–8522. https://doi.org/10.1016/j.eswa.2008.10.071
Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf
Manouselis N, Costopoulou C (2007) Analysis and classification of multi-criteria recommender systems. World Wide Web 10(4):415–441. https://doi.org/10.1007/s11280-007-0019-8
Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10059–10072. https://doi.org/10.1016/j.eswa.2012.02.038
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. https://doi.org/10.1016/j.eswa.2017.12.020
Quadrana M, Cremonesi P, Jannach D (2018) Sequence-aware recommender systems. ACM Comput Surv 51(4):1–36. https://doi.org/10.1145/3190616
Ricci F, Rokach L, Shapira B (2015). Recommender systems: Introduction and challenges. In: Recommender systems handbook. Springer, pp 1–34. https://doi.org/10.1007/978-1-4899-7637-6_1
Said A, Bellogín A (2014) Comparative recommender system evaluation. In: Proceedings of the 8th ACM conference on recommender systems. ACM, pp 129–136. https://doi.org/10.1145/2645710.2645746
Wang H, Lu Y, Zhai C (2010) Latent aspect rating analysis on review text data. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM. https://doi.org/10.1145/1835804.1835903
Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system. ACM Comput Surv 52(1):1–38. https://doi.org/10.1145/3285029
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Appendix: Selected studies
Appendix: Selected studies
Code | Author | Title | Year | Publication | Source |
---|---|---|---|---|---|
P1 | Liu, L.; Mehandjiev, N.; Xu, D.-L. | Multi-criteria service recommendation based on user criteria preferences | 2011 | Fifth ACM Conference on Recommender Systems | ACM Digital Library |
P2 | Shambour, Q.; Lu, J. | A hybrid multi-criteria semantic-enhanced collaborative filtering approach for personalized recommendations | 2011 | International Conferences on Web Intelligence and Intelligent Agent Technology | ACM Digital Library |
P3 | Jannach, D.; Karakaya, Z.; Gedikli, F. | Accuracy improvements for multi-criteria recommender systems | 2012 | 13th ACM Conference on Electronic Commerce | ACM Digital Library |
P4 | Hdioud, F.; Frikh, B.; Ouhbi, B. | Multi-criteria recommender systems based on multi-attribute decision making | 2013 | International Conference on Information Integration and Web-based Applications & Services | ACM Digital Library |
P5 | Choudhary, P.; Kant, V.; Dwivedi, P. | A particle swarm optimization approach to multi criteria recommender system utilizing effective similarity measures | 2017 | 9th International Conference on Machine Learning and Computing | ACM Digital Library |
P6 | Musto, C.; de Gemmis, M.; Semeraro, G.; Lops, P. | A multi-criteria recommender system exploiting aspect-based sentiment analysis of users’ reviews | 2017 | Eleventh ACM Conference on Recommender Systems | ACM Digital Library |
P7 | Park, Y. | Recommending personalized tips on new courses for guiding course selection | 2017 | South-East Conference | ACM Digital Library |
P8 | Sreepada, R. S.; Patra, B. K.; Hernando, A. | Multi-criteria recommendations through preference learning | 2017 | Fourth ACM IKDD Conferences on Data Sciences | ACM Digital Library |
P9 | Zheng, Y. | Criteria chains: A novel multi-criteria recommendation approach | 2017 | 22nd International Conference on Intelligent User Interfaces | ACM Digital Library |
P10 | Tallapally, D.; Sreepada, R. S.; Patra, B. K.; Babu, K. S. | User preference learning in multi-criteria recommendations using stacked auto encoders | 2018 | 12th ACM Conference on Recommender Systems | ACM Digital Library |
P11 | Zheng, Y.; Dave, T.; Mishra, N.; Kumar, H. | Fairness in reciprocal recommendations | 2018 | 26th Conference on User Modeling, Adaptation and Personalization | ACM Digital Library |
P12 | Niknafs, A.; Charkari, N. M.; Niknafs, A. A. | PROMETHEE-based recommender system for multi-sort recommendations in on-line stores | 2008 | Third International Conference on Digital Information Management | IEEE Xplore |
P13 | Hwang, C.-S.; Kao, Y.-C.; Yu, P. | Integrating multiple linear regression and multicriteria collaborative filtering for better recommendation | 2010 | International Conference on Computational Aspects of Social Networks | IEEE Xplore |
P14 | Liu, L.; Lecue, F.; Mehandjiev, N.; Xu, L. | Using context similarity for service recommendation | 2010 | IEEE Fourth International Conference on Semantic Computing | IEEE Xplore |
P15 | Shambour, Q.; Lu, J. | A framework of hybrid recommendation system for government-to-business personalized e-services | 2010 | Seventh International Conference on Information Technology: New Generations | IEEE Xplore |
P16 | Zarrinkalam, F.; Kahani, M. | A multi-criteria hybrid citation recommendation system based on linked data | 2012 | 2nd International eConference on Computer and Knowledge Engineering | IEEE Xplore |
P17 | Boulkrinat, S.; Hadjali, A.; Mokhtari, A. | Enhancing recommender systems prediction through qualitative preference relations | 2013 | 11th International Symposium on Programming and Systems | IEEE Xplore |
P18 | Samatthiyadikun, P.; Takasu, A.; Maneeroj, S. | Bayesian model for a multicriteria recommender system with support vector regression | 2013 | IEEE 14th International Conference on Information Reuse & Integration | IEEE Xplore |
P19 | Bokde, D. K.; Girase, S.; Mukhopadhyay, D. | An approach to a university recommendation by multi-criteria collaborative filtering and dimensionality reduction techniques | 2015 | IEEE International Symposium on Nanoelectronic and Information Systems | IEEE Xplore |
P20 | Sharma, Y.; Bhatt, J.; Magon, R. | A multi-criteria review-based hotel recommendation system | 2015 | IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing | IEEE Xplore |
P21 | Asawarangsee, T.; Maneeroj, S. | A novel aggregation technique for multi-criteria recommendation | 2016 | 13th International Joint Conference on Computer Science and Software Engineering | IEEE Xplore |
P22 | Ashley-Dejo, E.; Ngwira, S. M.; Zuva, T. | A context-aware proactive recommender system for tourist | 2016 | International Conference on Advances in Computing and Communication Engineering | IEEE Xplore |
P23 | Hassan, M.; Hamada, M. | Enhancing learning objects recommendation using multi-criteria recommender systems | 2016 | IEEE International Conference on Teaching, Assessment, and Learning for Engineering | IEEE Xplore |
P24 | Wijayanto, A.; Winarko, E. | Implementation of multi-criteria collaborative filtering on cluster using Apache Spark | 2016 | 2nd International Conference on Science and Technology-Computer | IEEE Xplore |
P25 | Hamada, M.; Odu, N. B.; Hassan, M. | A fuzzy-based approach for modelling preferences of users in multi-criteria recommender systems | 2018 | IEEE 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip | IEEE Xplore |
P26 | Mohamed, H.; Abdulsalam, L.; Mohammed, H. | Adaptive genetic algorithm for improving prediction accuracy of a multi-criteria recommender system | 2018 | IEEE 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip | IEEE Xplore |
P27 | Turk, A. M.; Bilge, A. | A robust multi-criteria collaborative filtering algorithm | 2018 | Innovations in Intelligent Systems and Applications | IEEE Xplore |
P28 | Manouselis, N.; Costopoulou, C. | Experimental analysis of design choices in multiattribute utility collaborative filtering | 2007 | International Journal of Pattern Recognition and Artificial Intelligence | ISI Web of Knowledge |
P29 | Yin, Z.; Yueting, Z.; Jiangqin, W.; Liang, Z. | Applying probabilistic latent semantic analysis to multi-criteria recommender system | 2009 | AI Communications | ISI Web of Knowledge |
P30 | Palanivel, K.; Sivakumar, R. | A study on implicit feedback in multicriteria e-commerce recommender system | 2010 | Journal of Electronic Commerce Research | ISI Web of Knowledge |
P31 | Dixit, V. S.; Mehta, H.; Bedi, P. | A proposed framework for group-based multi-criteria recommendations | 2014 | Applied Artificial Intelligence | ISI Web of Knowledge |
P32 | Mikeli, A.; Apostolou, D.; Despotis, D. | A new recommendation technique for interval scaled multi-criteria rating systems incorporating intensity of preferences | 2015 | Intelligent Decision Technologies | ISI Web of Knowledge |
P33 | Hamada, M.; Hassan, M. | Artificial neural networks and particle swarm optimization algorithms for preference prediction in multi-criteria recommender systems | 2018 | Informatics | ISI Web of Knowledge |
P34 | Chen, D.-N.; Hu, P. J.-H.; Kuo, Y.-R.; Liang, T.-P. | A web-based personalized recommendation system for mobile phone selection: Design, implementation, and evaluation | 2010 | Expert Systems with Applications | ScienceDirect |
P35 | Huang, S.-l. | Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods | 2011 | Electronic Commerce Research and Applications | ScienceDirect |
P36 | Liu, H.; He, J.; Wang, T.; Song, W.; Du, X. | Combining user preferences and user opinions for accurate recommendation | 2013 | Electronic Commerce Research and Applications | ScienceDirect |
P37 | Hu, Y.-C. | Nonadditive similarity-based single-layer perceptron for multi-criteria collaborative filtering | 2014 | Neurocomputing | ScienceDirect |
P38 | Li, Y.-M.; Chou, C.-L.; Lin, L.-F. | A social recommender mechanism for location-based group commerce | 2014 | Information Sciences | ScienceDirect |
P39 | Marin, L.; Moreno, A.; Isern, D. | Automatic preference learning on numeric and multi-valued categorical attributes | 2014 | Knowledge-Based Systems | ScienceDirect |
P40 | Nilashi, M.; bin Ibrahim, O.; Ithnin, N. | Hybrid recommendation approaches for multi-criteria collaborative filtering | 2014 | Expert Systems with Applications | ScienceDirect |
P41 | Son, L. H.; Thong, N. T. | Intuitionistic fuzzy recommender systems: An effective tool for medical diagnosis | 2015 | Knowledge-Based Systems | ScienceDirect |
P42 | Ali, M.; Son, L. H.; Thanh, N. D.; Minh, N. V. | A neutrosophic recommender system for medical diagnosis based on algebraic neutrosophic measures | 2017 | Applied Soft Computing | ScienceDirect |
P43 | Amoretti, M.; Belli, L.; Zanichelli, F. | UTravel: Smart mobility with a novel user profiling and recommendation approach | 2017 | Pervasive and Mobile Computing | ScienceDirect |
P44 | Choudhary, P.; Kant, V.; Dwivedi, P. | Handling natural noise in multi criteria recommender system utilizing effective similarity measure and particle swarm optimization | 2017 | Procedia Computer Science | ScienceDirect |
P45 | Kermany, N. R.; Alizadeh, S. H. | A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques | 2017 | Electronic Commerce Research and Applications | ScienceDirect |
P46 | Yuen, K. K. F. | The fuzzy cognitive pairwise comparisons for ranking and grade clustering to build a recommender system: An application of smartphone recommendation | 2017 | Engineering Applications of Artificial Intelligence | ScienceDirect |
P47 | Akcayol, M. A.; Utku, A.; Aydoğan, E.; Mutlu, B. | A weighted multi-attribute-based recommender system using extended user behavior analysis | 2018 | Electronic Commerce Research and Applications | ScienceDirect |
P48 | Castillo, A.; Meer, D. V.; Castellanos, A. | ExUP recommendations: Inferring user’s product metadata preferences from single-criterion rating systems | 2018 | Decision Support Systems | ScienceDirect |
P49 | Nilashi, M.; Ibrahim, O.; Yadegaridehkordi, E.; Samad, S.; Akbari, E.; Alizadeh, A. | Travelers decision making using online review in social network sites: A case on TripAdvisor | 2018 | Journal of Computational Science | ScienceDirect |
P50 | Núñez-Valdez, E. R.; Quintana, D.; Crespo, R. G.; Isasi, P.; Herrera-Viedma, E. | A recommender system based on implicit feedback for selective dissemination of ebooks | 2018 | Information Sciences | ScienceDirect |
P51 | Song, W.; Sakao, T. | An environmentally conscious PSS recommendation method based on users’ vague ratings: A rough multi-criteria approach | 2018 | Journal of Cleaner Production | ScienceDirect |
P52 | Wasid, M.; Ali, R. | An improved recommender system based on multi-criteria clustering approach | 2018 | Procedia Computer Science | ScienceDirect |
P53 | Adomavicius, G.; Kwon, Y. | New recommendation techniques for multicriteria rating systems | 2007 | IEEE Intelligent Systems | Scopus |
P54 | Lee, H.-H.; Teng, W.-G. | Incorporating multi-criteria ratings in recommendation systems | 2007 | IEEE International Conference on Information Reuse and Integration | Scopus |
P55 | Hwang, C.-S. | Genetic algorithms for feature weighting in multi-criteria recommender systems | 2010 | Journal of Convergence Information Technology | Scopus |
P56 | Lousame, F. P.; Sanchez, E. | Multicriteria predictors using aggregation functions based on item views | 2010 | 10th International Conference on Intelligent Systems Design and Applications | Scopus |
P57 | Tangphoklang, P.; Maneeroj, S.; Takasu, A. | Advanced representative and dynamic user profile based on MCDM for multi-criteria RS | 2010 | IADIS International Conference Information Systems | Scopus |
P58 | Akhtarzada, A.; Calude, C. S.; Hosking, J. | A multi-criteria metric algorithm for recommender systems | 2011 | Fundamenta Informaticae | Scopus |
P59 | Lakiotaki, K.; Matsatsinis, N. F.; Tsoukias, A. | Multicriteria user modeling in recommender systems | 2011 | IEEE Intelligent Systems | Scopus |
P60 | Palanivel, K.; Sivakumar, R. | A study on collaborative recommender system using fuzzy-multicriteria approaches | 2011 | International Journal of Business Information Systems | Scopus |
P61 | Shambour, Q.; Lu, J. | Integrating multi-criteria collaborative filtering and trust filtering for personalized recommender systems | 2011 | IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making | Scopus |
P62 | Samatthiyadikun, P.; Takasu, A.; Maneeroj, S. | Multicriteria collaborative filtering by Bayesian model-based user profiling | 2012 | IEEE 13th International Conference on Information Reuse & Integration | Scopus |
P63 | Premchaiswadi, W.; Poompuang, P. | Hybrid profiling for hybrid multicriteria recommendation based on implicit multicriteria information | 2013 | Applied Artificial Intelligence | Scopus |
P64 | Agathokleous, M.; Tsapatsoulis, N. | Learning user models in multi-criteria recommender systems | 2014 | Engineering Applications of Neural Networks | Scopus |
P65 | Bilge, A.; Kaleli, C. | A multi-criteria item-based collaborative filtering framework | 2014 | 11th International Joint Conference on Computer Science and Software Engineering | Scopus |
P66 | Hdioud, F.; Frikh, B.; Ouhbi, B. | Bootstrapping recommender systems based on a multi-criteria decision making approach | 2014 | International Conference on Next Generation Networks and Services | Scopus |
P67 | Hu, Y.-C. | A multicriteria collaborative filtering approach using the indifference relation and its application to initiator recommendation for group-buying | 2014 | Applied Artificial Intelligence | Scopus |
P68 | Manouselis, N.; Kyrgiazos, G.; Stoitsis, G. | Exploratory study of multi-criteria recommendation algorithms over technology enhanced learning datasets | 2014 | Journal of E-Learning and Knowledge Society | Scopus |
P69 | Pinandito, A.; Ananta, M. T.; Brata, K. C.; Fanani, L. | Alternatives weighting in analytic hierarchy process of mobile culinary recommendation system using fuzzy | 2015 | ARPN Journal of Engineering and Applied Sciences | Scopus |
P70 | Sneha, Y. S.; Mahadevan, G. | A novel approach to personalized recommender systems based on multi criteria ratings | 2015 | Research Journal of Applied Sciences, Engineering and Technology | Scopus |
P71 | Bankshinategh, B.; Spanakis, G.; Zaiane, O.; ElAtia, S. | A course recommender system based on graduating attributes | 2017 | 9th International Conference on Computer Supported Education | Scopus |
P72 | Goswami, A.; Dwivedi, P.; Kant, V. | Trust-enhanced multi-criteria recommender system | 2017 | Advances in Intelligent Systems and Computing | Scopus |
P73 | Hassan, M.; Hamada, M. | A neural networks approach for improving the accuracy of multi-criteria recommender systems | 2017 | Applied Sciences | Scopus |
P74 | Leal, F.; González-Vélez, H.; Malheiro, B.; Burguillo, J. C. | Profiling and rating prediction from multi-criteria crowd-sourced hotel ratings | 2017 | 31st European Conference on Modelling and Simulation | Scopus |
P75 | Majumder, G. S.; Dwivedi, P.; Kant, V. | Matrix factorization and regression-based approach for multi-criteria recommender system | 2017 | Information and Communication Technology for Intelligent Systems | Scopus |
P76 | Karacapilidis, N.; Hatzieleftheriou, L. | Exploiting similarity measures in multi-criteria based recommendations | 2003 | E-Commerce and Web Technologies | Springer Link |
P77 | Manouselis, N.; Costopoulou, C. | Preliminary study of the expected performance of MAUT collaborative filtering algorithms | 2008 | The Open Knowlege Society | Springer Link |
P78 | Matsatsinis, N. F.; Manarolis, E. A. | New hybrid recommender approaches: An application to equity funds selection | 2009 | Algorithmic Decision Theory | Springer Link |
P79 | Naak, A.; Hage, H.; Aïmeur, E. | A multi-criteria collaborative filtering approach for research paper recommendation in Papyres | 2009 | E-Technologies: Innovation in an Open World | Springer Link |
P80 | Bitonto, P. D.; Laterza, M.; Roselli, T.; Rossano, V. | Multi-criteria retrieval in cultural heritage recommendation systems | 2010 | Knowledge-Based and Intelligent Information and Engineering Systems | Springer Link |
P81 | Maneeroj, S.; Samatthiyadikun, P.; Chalermpornpong, W.; Panthuwadeethorn, S.; Takasu, A. | Ranked criteria profile for multi-criteria rating recommender | 2012 | Information Systems, Technology and Management | Springer Link |
P82 | Fan, J.; Xu, L. | A robust multi-criteria recommendation approach with preference-based similarity and support vector machine | 2013 | Advances in Neural Networks | Springer Link |
P83 | Jannach, D.; Zanker, M.; Fuchs, M. | Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations | 2014 | Information Technology & Tourism | Springer Link |
P84 | Nilashi, M.; Ibrahim, O. B.; Ithnin, N.; Zakaria, R. | A multi-criteria recommendation system using dimensionality reduction and neuro-fuzzy techniques | 2014 | Soft Computing | Springer Link |
P85 | Chen, T.; Chuang, Y. H. | Fuzzy and nonlinear programming approach for optimizing the performance of ubiquitous hotel recommendation | 2015 | Journal of Ambient Intelligence and Humanized Computing | Springer Link |
P86 | Li, S. T.; Pham, T. T.; Chuang, H. C.; Wang, Z.-W. | Does reliable information matter? Towards a trustworthy co-created recommendation model by mining unboxing reviews | 2015 | Information Systems and e-Business Management | Springer Link |
P87 | Parveen, R.; Kant, V.; Dwivedi, P.; Jaiswal, A. K. | Enhancing recommendation quality of a multi criterion recommender system using genetic algorithm | 2015 | Mining Intelligence and Knowledge Exploration | Springer Link |
P88 | Jhalani, T.; Kant, V.; Dwivedi, P. | A linear regression approach to multi-criteria recommender system | 2016 | Data Mining and Big Data | Springer Link |
P89 | Kant, V.; Jhalani, T.; Dwivedi, P. | Enhanced multi-criteria recommender system based on fuzzy Bayesian approach | 2017 | Multimedia Tools and Applications | Springer Link |
P90 | Ko, H.-G.; Ko, I.-Y.; Lee, D. | Multi-criteria matrix localization and integration for personalized collaborative filtering in IoT environments | 2017 | Multimedia Tools and Applications | Springer Link |
P91 | Leal, F.; Malheiro, B.; González-Vélez, H.; Burguillo, J. C. | Trust-based modelling of multi-criteria crowdsourced data | 2017 | Data Science and Engineering | Springer Link |
P92 | Ding, Y.; Li, S.; Yu, W. | Multi-criteria recommendation schemes based on factorization machines | 2018 | Cluster Computing | Springer Link |
P93 | Ding, Y.; Li, S.; Yu, W.; Wang, J.; Liu, M. | A unified neural model for review-based rating prediction by leveraging multi-criteria ratings and review text | 2018 | Cluster Computing | Springer Link |
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Monti, D., Rizzo, G. & Morisio, M. A systematic literature review of multicriteria recommender systems. Artif Intell Rev 54, 427–468 (2021). https://doi.org/10.1007/s10462-020-09851-4
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DOI: https://doi.org/10.1007/s10462-020-09851-4