Abstract
Recommender system plays an increasingly important role in identifying the individual’s preference and accordingly makes a personalized recommendation. Matrix factorization is currently the most popular model-based collaborative filtering (CF) method that achieves high recommendation accuracy. However, similarity computation hinders the development of CF-based recommendation systems. Preference obtained only depends on the explicit rating without considering the implicit content feature, which is the root cause of preference bias. In this paper, the content feature of items described by fuzzy sets is integrated into the similarity computation, which helps to improve the accuracy of user preference modeling. The importance of a user is then defined according to preferences, which serves as a baseline standards of the core users selection. Furthermore, core users based matrix factorization model (CU-FHR) is established, then genetic algorithm is used to predict the missing rating on items. Finally, MovieLens is used to test the performance of our proposed method. Experiments show CU-FHR achieves better accuracy in prediction compared with the other recommendation methods.









Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
Enquiries about data availability should be directed to the authors.
References
Aguilara J, Valdiviezo-Dıázb P, Riofrio G (2017) A general framework for intelligent recommender systems. Appl Comput Inform 13:147–160. https://doi.org/10.1016/j.aci.2016.08.002
Ali M, Jung LT, Abdel-Aty AH, Abubakar MY, Elhoseny M, Ali I (2020) Semantic-k-NN algorithm: an enhanced version of traditional k-NN algorithm. Expert Syst Appl 15:1113374. https://doi.org/10.1016/j.eswa.2020.113374
Anwaar F, Iltaf N, Afzal H, Nawaz R (2018) HRS-CE: a hybrid framework to integrate content embeddings in recommender systems for cold start items. J Comput Sci 29:9–18. https://doi.org/10.1016/j.jocs.2018.09.008
Ayub M, Ali Ghazanfar M, Mehmood Z, Alyoubi KH, Alfakeeh AS (2020) Unifying user similarity and social trust to generate powerful recommendations for smart cities using collaborating filtering-based recommender systems. Soft Comput 24:11071–11094. https://doi.org/10.1007/s00500-019-04588-x
Bobadilla J, Ortega F, Hernando A, Alcalá J (2011) Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowl-Based Syst 24:1310–1316. https://doi.org/10.1016/j.knosys.2011.06.005
Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132. https://doi.org/10.1016/j.knosys.2013.03.012
Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adap Inter 12(4):331–370. https://doi.org/10.1023/A:1021240730564
Candès EJ, Recht B (2009) Exact matrix completion via convex optimization. Found Comput Math 9:717–772. https://doi.org/10.1007/s10208-009-9045-5
Choudhary N, Minz S, Bharadwaj KK (2020) Circle-based group recommendation in social networks. Soft Comput. https://doi.org/10.1007/s00500-020-05356-y
Del Corso GM, Romani F (2019) Adaptive nonnegative matrix factorization and measure comparisons for recommender systems. Appl Math Comput 354:164–179. https://doi.org/10.1016/j.amc.2019.01.047
Duma M, Twala B (2018) Optimising latent features using artificial immune system in collaborative filtering for recommender systems. Appl Soft Comput 71:183–198. https://doi.org/10.1016/j.asoc.2018.07.001
Guan N, Tao D, Luo Z, Yuan B (2012) Online nonnegative matrix factorization with robust stochastic approximation. IEEE Trans Neural Netw Learn Syst 23(7):1087–1099. https://doi.org/10.1109/TNNLS.2012.2197827
Holland J (1975) Adaptation in nature and artificial systems. University of Michigan Press, Ann Arbor
Jelodar H, Wang Y, Xiao G, Rabbani M, Zhao R, Ayobi S, Hu P, Masood I (2020) Recommendation system based on semantic scholar mining and topic modeling on conference publications. Soft Comput. https://doi.org/10.1007/s00500-020-05397-3
Kermany NR, Alizadeh SH (2017) A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques. Electron Commer Res Appl 21:50–64. https://doi.org/10.1016/j.elerap.2016.12.005
Kilani Y, Otoom AF, Alsarhan A, Almaayah M (2018) A genetic algorithms-based hybrid recommender system of matrix factorization and neighborhood-based techniques. J Comput Sci 28:78–93. https://doi.org/10.1016/j.jocs.2018.08.007
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37
Krasnoshchok O, Lamo Y (2014) Extended content-boosted matrix factorization algorithm for recommender systems. Int Conf Knowl-Based Intell Inf Eng Syst-Proced Comput Sci 35:417–426. https://doi.org/10.1016/j.procs.2014.08.122
Lei C, Dai H, Yu Z, Li R (2020) A service recommendation algorithm with the transfer learning based matrix factorization to improve cloud security. Inf Sci 513:98–111. https://doi.org/10.1016/j.ins.2019.10.004
Liu B, Xiong H, Spiros P, Fu Y, Yao Z (2015) A general geographical probabilistic factor model for pointof interest recommendation. IEEE Trans Knowl Data Eng 27(5):1167–1179. https://doi.org/10.1109/TKDE.2014.2362525
Loepp B, Donkers T, Kleemann T, Ziegler J (2019) Interactive recommending with tag-enhanced matrix factorization (TagMF). Int J Hum Comput Stud 121:21–41. https://doi.org/10.1016/j.ijhcs.2018.05.002
Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32. https://doi.org/10.1016/j.dss.2015.03.008
Mao M, Lu J, Zhang G, Zhang J (2017) Multirelational social recommendations via multigraph ranking. IEEE Trans Cybern 47(12):4049–4061. https://doi.org/10.1109/TCYB.2016.2595620
Martín-Vicente MI, Gil-Solla A, Ramos-Cabrer M, Pazos-Arias José J, Blanco-Fernández Y, López-Nores M (2014) A semantic approach to improve neighborhood formation in collaborative recommender systems. Expert Syst Appl 41(17):7776–7788. https://doi.org/10.1016/j.eswa.2014.06.038
Najafabadi MK, Mohamed AH, Mahrin MN (2019) A survey on data mining techniques in recommender systems. Soft Comput 23:627–654. https://doi.org/10.1007/s00500-017-2918-7
Navgaran DZ, Moradi P, Akhlaghian F (2013) Evolutionary based matrix factorization method for collaborative filtering systems. In: 2013 21st Iranian conference on electrical engineering, ICEE 2013. Doi: https://doi.org/10.1109/IranianCEE.2013.6599844
Ntoutsi E, Stefanidis K, Norvag K, Kriegel HP (2012) Fast group recommendations by applying user clustering. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Doi: https://doi.org/10.1007/978-3-642 -34002-4_10.
Palomaresa I, Brownec F, Davis P (2018) Multi-view fuzzy information fusion in collaborative filtering recommender systems: application to the urban resilience domain. Data Knowl Eng 113:64–80. https://doi.org/10.1016/j.datak.2017.10.002
Schafer J, Frankkowski D, Herlocker J (2007) Collaborative filtering recommender systems. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), pp. 291-324. Doi: https://doi.org/10.1007/978-3-540- 72079-9-9.
Selvi C, Sivasankar E (2019) A novel optimization algorithm for recommender system using modified fuzzy c-means clustering approach. Soft Comput 23:1901–1916. https://doi.org/10.1007/s00500-017-2899-6
Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell. https://doi.org/10.1155/2009/421425
Viktoratos I, Tsadiras A, Bassiliades N (2018) Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems. Expert Syst Appl 101:78–90. https://doi.org/10.1016/j.eswa.2018.01.044
Yera R, MartÍnez L (2017) Fuzzy tools in recommender systems: a survey. Int J Comput Intell Syst 10:776–803. https://doi.org/10.2991/ijcis.2017.10.1.52
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci 8(3):199–249. https://doi.org/10.1016/0020-0255(75)90036-5
Zenebea A, Norciob AF (2009) Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets Syst 160:76–94. https://doi.org/10.1016/j.fss.2008.03.017
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 235:117–129. https://doi.org/10.1016/j.ins.2013.01.025
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. https://doi.org/10.1109/TNNLS.2018.2875144
Acknowledgements
This work is supported by the National Natural Science Foundation of China (72101082, 62076088), the Natural Science Foundation of Hebei Province (F2021208011).
Funding
The authors have not disclosed any funding.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Jin, C., Mi, J., Li, F. et al. Hybrid recommender system with core users selection. Soft Comput 26, 13925–13939 (2022). https://doi.org/10.1007/s00500-022-07424-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-022-07424-x