Abstract
Recommender systems are currently successful solutions for facilitating access for online users to the information that fits their preferences and needs in overloaded search spaces. In the last years several methodologies have been developed to improve their performance. This paper is focused on developing a review on the use of fuzzy tools in recommender systems, for detecting the more common research topics and also the research gaps, in order to suggest future research lines for boosting the current developments in fuzzy-based recommender systems. Specifically, it is developed an analysis of the papers focused at such aim, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas.
Article PDF
Avoid common mistakes on your manuscript.
References
A. Abbas, L. Zhang, and S. U. Khan. A survey on context-aware recommender systems based on computational intelligence techniques. Computing, 97(7): 667–690, 2015.
M. N. M. Adnan, M. R. Chowdury, I. Taz, T. Ahmed, and R. M. Rahman. Content based news recommendation system based on fuzzy logic. In ICIEV, pages 1–6. IEEE, 2014.
G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6): 734–749, 2005.
S. Aguzzoli, P. Avesani, and B. Gerla. A logical framework for fuzzy collaborative filtering. In 10TH IEEE International Conference on Fuzzy Systems, pages 1043–1046. IEEE, 2001.
H. Al-Qaheri and S. Banerjee. Design and implementation of a policy recommender system towards social innovation: An experience with hybrid machine learning. In Intelligent Data Analysis and Applications, pages 237–250. Springer, 2015.
M. Y. H. Al-Shamri and N. H. Al-Ashwal. Fuzzy-weighted Pearson Correlation Coefficient for Collaborative Recommender Systems. In ICEIS, pages 409–414, 2013.
M. Y. H. Al-Shamri and K. K. Bharadwaj. A compact user model for hybrid movie recommender system. In ICCIMA, pages 519–524, 2007.
M. Y. H. Al-Shamri and K. K. Bharadwaj. Fuzzygenetic approach to recommender systems based on a novel hybrid user model. Expert Systems with Applications, 35(3):1386–1399, 2008.
D. Anand and B. S. Mampilli. Folksonomy-based fuzzy user profiling for improved recommendations. Expert Systems with Applications, 41(5):2424–2436, 2014.
S. Ashkezari-T and M.-R. Akbarzadeh-T. Fuzzy-Bayesian Network Approach to Genre-based Recommender Systems. In FUZZ-IEEE 2010, 2010.
T. Bai, B. Ding, Y. Wang, J. Ning, and L. Huang. A collaborative filtering algorithm based on citation information. In Logistics Engineering, Management and Computer Science, 2015 International Conference on, pages 952–956. Atlantis-Press, 2015.
M. Balabanović and Y. Shoham. Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3):66–72, 1997.
L. Banda and K. Bharadwaj. An approach to enhance the quality of recommendation using collaborative tagging. International Journal of Computational Intelligence Systems, 7(4):650–659, 2014.
P. Bedi and S. K. Agarwal. A situation-aware proactive recommender system. In HIS, pages 85–89. IEEE, 2012.
P. Bedi and P. Vashisth. Empowering recommender systems using trust and argumentation. Information Sciences, 279:569–586, 2014.
J. C. Bezdek, R. Ehrlich, and W. Full. Fcm: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3):191–203, 1984.
K. K. Bharadwaj and M. Y. H. Al-Shamri. Fuzzy computational models for trust and reputation systems. Electronic Commerce Research and Applications, 8 (1):37–47, 2009.
A. Bilge and H. Polat. A comparison of clustering-based privacy-preserving collaborative filtering schemes. Applied Soft Computing, 13(5):2478–2489, 2013.
C. Birtolo and D. Ronca. Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and trust. Expert Systems with Applications, 40(17):6997–7009, 2013.
J. Bobadilla, F. Ortega, and A. Hernando. A collaborative filtering similarity measure based on singularities. Information Processing & Management, 48(2): 204–217, 2012.
J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez. Recommender systems survey. Knowledge-Based Systems, 46(0):109 – 132, 2013. ISSN 0950-7051.
R. Burke. Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12(4):331–370, 2002.
P. G. Campos, F. Díez, and I. Cantador. Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Modeling and User-Adapted Interaction, 24(1-2):67–119, 2014.
E. J. Castellano, L. Martínez, and P. J. Sánchez. Orieb, a linguistic crs for supporting decision making in academic orientation. In FLINS, volume 21, page 24. World Scientific, 2008.
J. Castro, R. Yera Toledo, and L. Martínez. An empirical study of natural noise management in group recommendation systems. Decision Support Systems, 94: 1–11, 2017.
J. Castro, F. J. Quesada, I. Palomares, and L. Martínez. A Consensus-Driven Group Recommender System. International Journal of Intelligent Systems, 30(8): 887–906, 2015.
R.-M. Chao, J.-T. Huang, and C.-W. Yang. The study of knowledge service-oriented recommendation mechanism-a case of e-learning platform. In ICMLC, volume 4, pages 2228–2233. IEEE, 2005.
C. Chen and W. Tai. A user preference classification method in information recommendation system. In ICEB, pages 1091–1096, 2004.
D. Chen, Y. Ying, and S. Gong. A collaborative filtering algorithm based on rough set and fuzzy clustering. In FSKD, volume 1, pages 17–20. IEEE, 2008.
L.-C. Cheng and H.-A. Wang. A fuzzy recommender system based on the integration of subjective preferences and objective information. Applied Soft Computing, 18:290–301, 2014.
C. Christakou, S. Vrettos, and A. Stafylopatis. A hybrid movie recommender system based on neural networks. International Journal on Artificial Intelligence Tools, 16(5):771–792, 2007.
C. Cornelis, J. Lu, X. Guo, and G. Zhang. One-and-only item recommendation with fuzzy logic techniques. Information Sciences, 177(22):4906–4921, 2007.
L. M. de Campos, J. M. Fernandez-Luna, and J. F. Huete. A collaborative recommender system based on probabilistic inference from fuzzy observations. Fuzzy Sets and Systems, 159(12):1554–1576, 2008.
M. de Gemmis, P. Lops, C. Musto, F. Narducci, and G. Semeraro. Semantics-aware content-based recommender systems. In Recommender Systems Handbook, pages 119–159. Springer US, 2015.
M. K. K. Devi and P. Venkatesh. Smoothing approach to alleviate the meager rating problem in collaborative recommender systems. Future Generation Computer Systems, 29(1):262–270, 2013.
Y. Djaghloul, V. Groues, and Y. Naudet. Combining situation and content similarities in fuzzy based interest matchmaking mechanism. In SMAP, pages 9–14. IEEE, 2012.
M. D. Ekstrand, J. T. Riedl, and J. A. Konstan. Collaborative filtering recommender systems. Foundations and Trends in Human-Computer Interaction, 4(2):81–173, 2011.
M. H. Esfahani and F. K. Alhan. New hybrid recommendation system based on c-means clustering method. In IKT, pages 145–149. IEEE, 2013.
P. Fang and S. Zheng. A research on fuzzy formal concept analysis based collaborative filtering recommendation system. In KAM, volume 3, pages 352–355. IEEE, 2009.
G. Fenza, E. Fischetti, D. Furno, and V. Loia. A hybrid context aware system for tourist guidance based on collaborative filtering. In FUZZ-IEEE, pages 131–138, 2011.
M. Gao, K. Liu, and Z. Wu. Personalisation in web computing and informatics: Theories, techniques, applications, and future research. Information Systems Frontiers, 12(5):607–629, 2010.
V. C. Gerogiannis, A. Karageorgos, L. Liu, and C. Tjortjis. Personalised fuzzy recommendation for high involvement products. In IEEE SMC, pages 4884–4890. IEEE, 2013.
C. Guan, K. K. F. Yuen, and F. Coenen. Towards an intuitionistic fuzzy agglomerative hierarchical clustering algorithm for music recommendation in folkson-omy. In IEEE SMC, pages 2039–2042. IEEE, 2015.
A. Gunawardana and G. Shani. A survey of accuracy evaluation metrics of recommendation tasks. Journal of Machine Learning Research, 10:2935–2962, 2009.
H. Q. He and Z. L. Fan. An Improved Collaborative Filtering Recommendation Algorithm Based on Co-clustering. In AETIE, pages 508–515, 2015.
F. Herrera and L. Martínez. A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on fuzzy systems, 8(6):746–752, 2000.
J. H. Holland. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press, 1975.
K. Honda, I. Hidetomo, and A. Notsu. A sequential learning algorithm for collaborative filtering with linear fuzzy clustering. In IEEE SMC, volume 2, pages 1056–1061. IEEE, 2006.
K. Honda, A. Notsu, and H. Ichihashi. Collaborative filtering by sequential extraction of user-item clusters based on structural balancing approach. In FUZZ-IEEE, pages 1540–1545. IEEE, 2009.
T. Horváth. A model of user preference learning for content-based recommender systems. Computing and informatics, 28(4):453–481, 2009.
Y.-C. Hu. Nonadditive similarity-based single-layer perceptron for multi-criteria collaborative filtering. Neurocomputing, 129:306–314, 2014.
Y.-C. Hu, Y.-J. Chiu, Y.-L. Liao, and Q. Li. A fuzzy similarity measure for collaborative filtering using nonadditive grey relational analysis. Journal of Grey System, 27(2), 2015.
H.-H. Huang, H.-C. Yang, and E. H.-C. Lu. A Fuzzy-Rough Set based Ontology for Hybrid Recommendation. In ICCE-TW, pages 358–359, 2015.
J.-S. Jang. Anfis: adaptive-network-based fuzzy inference system. IEEE transactions on Systems, Man, and Cybernetics, 23(3):665–685, 1993.
T. Jeon, J. Cho, S. Lee, G. Baek, and S. Kim. A movie rating prediction system of user propensity analysis based on collaborative filtering and fuzzy system. In FUZZ-IEEE, pages 507–511. IEEE, 2009.
A. Jøsang and R. Ismail. The beta reputation system. In Proceedings of the 15th bled electronic commerce conference, volume 5, pages 2502–2511, 2002.
V. Kant and K. K. Bharadwaj. Enhancing Recommendation Quality of Content-based Filtering through Collaborative Predictions and Fuzzy Similarity Measures. In ICMOC, pages 939–944, 2012.
V. Kant and K. K. Bharadwaj. Fuzzy Computational Models of Trust and Distrust for Enhanced Recommendations. International Journal of Intelligent Systems, 28(4):332–365, 2013.
V. Kant and K. K. Bharadwaj. Integrating collaborative and reclusive methods for effective recommendations: a fuzzy bayesian approach. International Journal of Intelligent Systems, 28(11):1099–1123, 2013.
N. Karacapilidis and L. Hatzieleftheriou. Exploiting similarity measures in multi-criteria based recommendations. In EC-WEB, pages 424–434, 2003.
R. Katarya and O. P. Verma. A collaborative recom-mender system enhanced with particle swarm optimization technique. Multimedia Tools and Applications, pages 1–15, 2016.
W. Kim, I.-J. Ko, J.-S. Yoon, and G.-Y. Kim. Inference of recommendation information on the internet using improved fam. Future Generation Computer Systems, 20(2):265–273, 2004.
A. Klašnja-Milićević, M. Ivanović, and A. Nanopoulos. Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 44(4):571–604, 2015.
M. Komkhao, J. Lu, Z. Li, and W. A. Halang. Incremental collaborative filtering based on Mahalanobis distance and fuzzy membership for recommender systems. International Journal of General Systems, 42 (1):41–66, 2013.
J. A. Konstan and J. Riedl. Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction, 22(1-2):101–123, 2012.
H. Koohi and K. Kiani. User based Collaborative Filtering using fuzzy C-means. Measurement, 91:134–139, 2016.
P. Ladyzynski and P. Grzegorzewski. Vague preferences in recommender systems. Expert Systems with Applications, 42(24):9402–9411, 2015.
Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521:436–444, 2015.
S. Lee. Personal recommendation based on a user’s understanding. Computer Applications in Engineering Education, 20(1):62–71, 2012.
C. W.-k. Leung, S. C.-f. Chan, and F.-l. Chung. A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowledge and Information Systems, 10(3):357–381, 2006.
J.-h. Li, X.-s. Li, H.-l. Liu, X.-j. Han, and J. Zhang. Fuzzy collaborative filtering approach based on semantic distance. In Fuzzy Information and Engineering Volume 2, pages 187–195. Springer, 2009.
S. Linda and K. K. Bharadwaj. A fuzzy trust enhanced collaborative filtering for effective context-aware rec-ommender systems. In Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 2, pages 227–237. Springer, 2016.
H. Liu and Z. Yin. Applying multiple agents to fuzzy collaborative filtering. In 2009 International Conference on E-Business and Information System Security, pages 1–5. IEEE, 2009.
H. Liu, Z. Hu, A. Mian, H. Tian, and X. Zhu. A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems, 56: 156–166, 2014.
P. Lops, M. De Gemmis, and G. Semeraro. Content-based recommender systems: State of the art and trends. In Recommender systems handbook, pages 73–105. Springer, 2011.
J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang. Recommender system application developments: a survey. Decision Support Systems, 74:12–32, 2015.
J. Lu, Q. Shambour, Y. Xu, Q. Lin, and G. Zhang. A Web-Based Personalized Business Partner Recommendation System Using Fuzzy Semantic Techniques. Computational Intelligence, 29(1):37–69, 2013.
M. Mao, J. Lu, G. Zhang, and J. Zhang. A Fuzzy Content Matching-based e-Commerce Recommendation Approach. In FUZZ-IEEE), 2015.
L. Martínez, L. G. Pérez, and M. Barranco. A multigranular linguistic content-based recommendation model. International Journal of Intelligent Systems, 22(5):419–434, 2007.
L. Martínez, M. J. Barranco, L. G. Perez, and M. Espinilla. A knowledge-based recommender system with multigranular linguistic information. International Journal of Computational Intelligence Systems, 1(3):225–236, 2008.
L. Martínez, D. Ruan, and F. Herrera. Computing with words in decision support systems: an overview on models and applications. International Journal of Computational Intelligence Systems, 3(4):382–395, 2010.
L. Martínez, F. Herrera, et al. An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges. Information Sciences, 207:1–18, 2012.
L. Martínez, J. Castro, and R. Yera. Managing natural noise in recommender systems. In TPNC, pages 3–17. Springer, 2016.
L. Martínez, L. G. Perez, M. Barranco, and M. Espinilla. Improving the effectiveness of knowledge based recommender systems using incomplete linguistic preference relations. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 16(2):33–56, 2008.
M. B. Menhaj and S. Jamalzehi. Scalable user similarity estimation based on fuzzy proximity for enhancing accuracy of collaborative filtering recommendation. In ICCIA, pages 220–225. IEEE, 2016.
S. Min and I. Han. Dynamic fuzzy clustering for recommender systems. In PAKDD, pages 480–485, 2005.
N. Mittal, M. Govil, R. Nayak, G. R, and D. Das. A hybrid clustering based filtering approach with efficient sequencing. In Proceedings of the International MultiConference of Engineers and Computer Scientists, volume 1. IAENG, 2008.
J. M. Morales-del Castillo, E. Peis, J. M. Moreno, and E. Herrera-Viedma. D-fussion: A semantic selective disssemination of information service for the research community in digital libraries. Information Research: An International Electronic Journal, 14(2), 2009.
C. Musto, G. Semeraro, P. Lops, M. de Gemmis, and G. Lekkas. Personalized finance advisory through case-based recommender systems and diversification strategies. Decision Support Systems, 77:100–111, 2015.
D. A. Nguyen and T. H. Duong. Video recommendation using neuro-fuzzy on social tv environment. In Advanced Computational Methods for Knowledge Engineering, pages 291–298. Springer, 2015.
M. Nilashi, O. bin Ibrahim, and N. Ithnin. Multicriteria collaborative filtering with high accuracy using higher order singular value decomposition and neuro-fuzzy system. Knowledge-Based Systems, 60: 82–101, 2014.
M. Nilashi, O. bin Ibrahim, N. Ithnin, and N. H. Sarmin. A multi-criteria collaborative filtering rec-ommender system for the tourism domain using expectation maximization (em) and pca-anfis. Electronic Commerce Research and Applications, 14(6): 542–562, 2015.
M. Nilashi, O. bin Ibrahim, and N. Ithnin. Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications, 41(8): 3879–3900, 2014.
X. Ning, C. Desrosiers, and G. Karypis. A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook, pages 37–76. Springer US, 2015.
J. M. Noguera, M. J. Barranco, R. J. Segura, and L. Martínez. A mobile 3D-GIS hybrid recommender system for tourism. Information Sciences, 215:37–52, 2012.
I. Pardines, V. López, A. Sanmartín, M. O. de Toledo, and C. Fernández. Collaborative recommendation system for environmental activities management mobile application. In ISKE, pages 327–335. Springer, 2014.
M. J. Pazzani. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6):393–408, 1999.
J. Pinho Lucas, A. Laurent, M. N. Moreno, and M. Teisseire. A fuzzy associative classification approach for recommender systems. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 20(4):579–617, 2012.
M. A. Pinto, R. Tanscheit, and M. Vellasco. Hybrid recommendation system based on collaborative filtering and fuzzy numbers. In FUZZ-IEEE, pages 1–6. IEEE, 2012.
C. Porcel, A. L pez-Herrera, and E. Herrera-Viedma. A recommender system to promoto collaborative research groups in an academic context. In FLINS, volume 21, page 24. World Scientific, 2008.
L. Qiao and R. Zhang. Personalized recommendation algorithm based on situation awareness. In LISS, pages 1–4. IEEE, 2015.
S. Queiroz, F. de Carvalho, G. Ramalho, and V. Cor-ruble. Making recommendations for groups using collaborative filtering and fuzzy majority. In SBIA, pages 248–258, 2002.
M. Ramezani and F. Yaghmaee. A novel video recommendation system based on efficient retrieval of human actions. Physica A, 457:607–623, 2016.
J. A. Recio-García, B. Diaz-Agudo, S. Gonzalez-Sanz, and L. Quijano Sanchez. Distributed Deliberative Recommender Systems. In Transactions on Computational Collective Intelligence I, pages 121–142, 2010.
M. Z. Reformat and R. R. Yager. Suggesting Recommendations Using Pythagorean Fuzzy Sets illustrated Using Netflix Movie Data. In Information Processing and Management of Uncertainty in Knowledge-Based Systems, PT I, pages 546–556, 2014.
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In ACM CSCW, pages 175–186, 1994.
R. M. Rodríguez and L. Martínez. An analysis of symbolic linguistic computing models in decision making. International Journal of General Systems, 42(1):121–136, 2013.
R. M. Rodríguez, L. Martínez, and F. Herrera. Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20(1):109–119, 2012.
R. M. Rodríguez, M. Espinilla, P. J. Sanchez, and L. Martínez-Lopez. Using linguistic incomplete preference relations to cold start recommendations. Internet Research, 20(3):296–315, 2010.
R. M. Rodríguez, A. Labella, and L. Martínez. An Overview on Fuzzy Modelling of Complex Linguistic Preferences in Decision Making. International Journal of Computational Intelligence Systems, 9(1, SI): 81–94, 2016.
N. Sahoo, P. V. Singh, and T. Mukhopadhyay. A hidden markov model for collaborative filtering. MIS Quarterly, 36(4):1329–1356, 2012.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, pages 285–295. ACM, 2001.
J. B. Schafer, J. A. Konstan, and J. Riedl. E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1-2):115–153, 2001.
V. Schlecht and W. Gaul. Fuzzy two-mode clustering vs. collaborative filtering. In Classification - The Ubiquitous Challenge, pages 410–417, 2005.
J. Serrano-Guerrero, E. Herrera-Viedma, J. A. Olivas, A. Cerezo, and F. P. Romero. A google wave-based fuzzy recommender system to disseminate information in university digital libraries 2.0. Information Sciences, 181(9):1503–1516, 2011.
J. Sobecki, E. Babiak, and M. Słanina. Application of hybrid recommendation in web-based cooking assistant. In KES, pages 797–804. Springer, 2006.
L. H. Son. HU-FCF: A hybrid user-based fuzzy collaborative filtering method in Recommender Systems. Expert Systems with Applications, 41(15):6861–6870, 2014.
L. H. Son and N. T. Thong. Intuitionistic fuzzy recom-mender systems: An effective tool for medical diagnosis. Knowledge-Based Systems, 74:133–150, 2015.
L. H. Son, N. T. H. Minh, K. M. Cuong, and N. V. Canh. An application of fuzzy geographically clustering for solving the cold-start problem in recommender systems. In SoCPaR, pages 44–49. IEEE, 2013.
X. Su and T. M. Khoshgoftaar. A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009:4, 2009.
B. Suryavanshi, N. Shiri, and S. Mudur. Improving the effectiveness of model based recommender systems for highly sparse and noisy web usage data. In WI, pages 618–621, 2005.
Y. Teng, L. Zhang, Y. Tian, and X. Li. A novel fahp based book recommendation method by fusing apriori rule mining. In ISKE, pages 237–243. IEEE, 2015.
N. T. Thong and L. H. Son. HIFCF: An effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis. Expert Systems with Applications, 42(7): 3682–3701, 2015.
S. Tiwari and S. Kaushik. Crowdsourcing Based Fuzzy Information Enrichment of Tourist Spot Recommender Systems. In ICCSA, pages 559–574, 2015.
K. Treerattanapitak and C. Jaruskulchai. Exponential fuzzy c-means for collaborative filtering. Journal of Computer Science and Technology, 27(3):567–576, 2012.
C.-H. Tsai. A fuzzy-based personalized recommender system for local businesses. In ACM HT, pages 297–302. ACM, 2016.
S. Tyagi and K. K. Bharadwaj. Trust-enhanced recommender system based on case-based reasoning and collaborative filtering. In ICPCES, pages 1–4. IEEE, 2012.
B. Veloso, B. Malheiro, and J. C. Burguillo. A multi– agent brokerage platform for media content recommendation. International Journal of Applied Mathematics and Computer Science, 25(3):513–527, 2015.
S. K. Verma, N. Mittal, and B. Agarwal. Hybrid Recommender System based on Fuzzy Clustering and Collaborative Filtering. In ICCCT, pages 116–120, 2013.
P. Victor, C. Cornelis, M. De Cock, and P. P. da Silva. Gradual trust and distrust in recommender systems. Fuzzy Sets and Systems, 160(10):1367–1382, 2009.
J. Vimali and Z. S. Taj. Fcm based cf: An efficient approach for consolidating big data applications. In International Conference on Innovation Information in Computing Technologies, pages 1–7. IEEE, 2015.
M. G. Vozalis and K. G. Margaritis. Using svd and demographic data for the enhancement of generalized collaborative filtering. Information Sciences, 177(15): 3017–3037, 2007.
R. Wang. Hybrid recommendation based on fuzzy clustering and data filling. In Information Computing And Automation: (In 3 Volumes), pages 59–63. World Scientific, 2008.
W. Wang, J. Lu, and G. Zhang. A New Similarity Measure-Based Collaborative Filtering Approach for Recommender Systems. In ISKE, pages 443–452, 2014.
W.-J. Wang. New similarity measures on fuzzy sets and on elements. Fuzzy sets and systems, 85(3):305–309, 1997.
M. Wasid and V. Kant. A Particle Swarm Approach to Collaborative Filtering based Recommender Systems through Fuzzy Features. In ICDMW, pages 440–448, 2015.
B. Widrow and M. A. Lehr. 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proceedings of the IEEE, 78(9):1415–1442, 1990.
D. Wu, G. Zhang, and J. Lu. A fuzzy preference tree-based recommender system for personalized business-to-business e-services. IEEE Transactions on Fuzzy Systems, 23(1):29–43, 2015.
D. Wu, J. Lu, and G. Zhang. A Fuzzy Tree Matching-Based Personalized E-Learning Recommender System. IEEE Transactions on Fuzzy Systems, 23(6): 2412–2426, 2015.
I. C. Wu and W.-H. Hwang. A genre-based fuzzy inference approach for effective filtering of movies. Intelligent Data Analysis, 17(6):1093–1113, 2013.
Z. Wu, Y. Chen, and T. Li. Personalized recommendation based on the improved similarity and fuzzy clustering. In ISEEE, volume 2, pages 1353–1357. IEEE, 2014.
Q. Xu, J. Wu, and Q. Chen. A novel mobile personalized recommended method based on money flow model for stock exchange. Mathematical Problems in Engineering, 2014, 2014.
S. Xu and J. Watada. A Method for Hybrid Personalized Recommender based on Clustering of Fuzzy User Profiles. In FUZZ-IEEE, pages 2171–2177, 2014.
R. Yager. On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on Systems, Man and Cybernetics, 18 (1):183–190, 1988.
R. Yager. Fuzzy logic methods in recommender systems. Fuzzy Sets and Systems, 136(2):133–149, 2003.
W.-S. Yang and Y.-R. Lin. A task-focused literature recommender system for digital libraries. Online Information Review, 37(4):581–601, 2013.
R. Yera Toledo and Y. Caballero Mota. An e-learning collaborative filtering approach to suggest problems to solve in programming online judges. International Journal of Distance Education Technologies, 12(2): 51–65, 2014.
R. Yera Toledo, Y. Caballero Mota, and L. Martínez. Correcting noisy ratings in collaborative recommender systems. Knowledge-Based Systems, 76:96–108, 2015.
R. Yera Toledo, J. Castro, and L. Martínez. A fuzzy model for managing natural noise in recommender systems. Applied Soft Computing, 40:187–198, 2016.
L. Zadeh. The concept of a linguistic variable and its applications to approximatereasoning. Part I. Information Sciencies, 8:199–249, 1975.
L. A. Zadeh. Fuzzy sets. Information and control, 8 (3):338–353, 1965.
A. Zenebe and A. F. Norcio. Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets and Systems, 160(1):76–94, 2009.
S. Zhang, C. Xi, Y. Wang, W. Zhang, and Y. Chen. A new method for e-government procurement using collaborative filtering and bayesian approach. The Scientific World Journal, 2013, 2013.
X. Zhang, W. Ma, and L. Chen. New similarity of triangular fuzzy number and its application. The Scientific World Journal, 2014, 2014.
Z. Zhang, H. Lin, K. Liu, D. Wu, G. Zhang, and J. Lu. A hybrid fuzzy-based personalized recommender system for telecom products/services. Information Sciences, 235:117–129, 2013.
W. X. Zhao, S. Li, Y. He, L. Wang, J.-R. Wen, and X. Li. Exploring demographic information in social media for product recommendation. Knowledge and Information Systems, 49(1):61–89, 2015.
X. W. Zhao, Y. Guo, Y. He, H. Jiang, Y. Wu, and X. Li. We know what you want to buy: a demographic-based system for product recommendation on microblogs. In KDD, pages 1935–1944. ACM, 2014.
H.-J. Zimmermann. Fuzzy set theory and its applications. Springer Science & Business Media, 2001.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
About this article
Cite this article
Yera, R., Martínez, L. Fuzzy Tools in Recommender Systems: A Survey. Int J Comput Intell Syst 10, 776–803 (2017). https://doi.org/10.2991/ijcis.2017.10.1.52
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.2991/ijcis.2017.10.1.52