A Review of Movie Recommendation System Limitations, Survey and Challenges
Article Sidebar
Main Article Content
Mahesh Goyani
Government Engineering College (Modasa, Índia). Department of Computer Engineering
Neha Chaurasiya
Government Engineering College (Modasa, Índia). Department of Computer Engineering
Recommendation System is a major area which is very popular and useful for people to take proper decision. It is a method that helps user to find out the information which is beneficial for the user from variety of data available. When it comes to Movie Recommendation System, recommendation is done based on similarity between users (Collaborative Filtering) or by considering particular user's activity (Content Based Filtering) which he wants to engage with. So to overcome the limitations of collaborative and content based filtering generally, combination of collaborative and content based filtering is used so that a better recommendation system can be developed. Also various similarity measures are used to find out similarity between users for recommendation. In this paper, we have reviewed different similarity measures. Various companies like face book which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends products etc. use recommendation system to increase their profit and also benefit their customers. This paper mainly concentrates on the brief review of the different techniques and its methods for movie recommendation, so that research in recommendation system can be explored.
Paraules clau
Recommendation system, Hybrid filtering, Matrix factorization, SVD, Similarity measures
Article Details
Com citar
Goyani, Mahesh; Chaurasiya, Neha. «A Review of Movie Recommendation System: Limitations, Survey and Challenges». ELCVIA: electronic letters on computer vision and image analysis, 2020, vol.VOL 19, núm. 3, p. 18-37, https://raco.cat/index.php/ELCVIA/article/view/373942.
Referències
J. A. Konstan and J. Riedl, ”Recommender systems: From algorithms to user experience”,User Modelingand User-Adapted Interaction22(1-2):101-123,2012. DOI: http://dx.doi.org/10.1007/s11257-011-9112-x
R. Katarya and O. P. Verma, ”An effective collaborative movie recommender system with cuckoo search”,Egyptian Informatics Journal18(2):105-112, 2017. DOI: http://dx.doi.org/10.1016/j.eij.2016.10.002
S. H. Min and I. Han, ”Detection of the customer time-variant pattern for improving recommender sys-tems”,Expert Systems with Applications28(2):189-199, 2005.
S. Wattal, Y. Hong, M. Mandviwalla, and A. Jain, ”Technology diffusion in the society: Analyzing digitaldivide in the context of social class”,IEEE Proc. of 44th Hawaii International Conference on SystemSciences, 1-10, 2011. DOI: http://dx.doi.org/10.1109/HICSS.2011.398
M. Goldmann and G. Kreitz, ”Measurements on the spotify peer-assisted music-on-demand stream-ing system”,IEEE International Conference on Peer-to-Peer Computing, 206-211, 2011. DOI:http://dx.doi.org/10.1109/P2P.2011.6038737
P. N. V. Kumar and V. R. Reddy, ”A Survey on Recommender Systems (RSS) and Its Applications”,International Journal of Innovative Research in Computer and Communication Engineering2(8):5254-5260, 2014.
C. A. Gomez-Uribe and N. Hunt, ”The Netflix Recommender System: Algorithms, Business Value,and Innovation”, ACM Transactions on Management Information Systems6(4):1-19, 2015. DOI:http://dx.doi.org/10.1145/2843948
O.Celma,MusicRecommendation,Springer,Berlin,Heidelberg,43-85,2010.DOI:http://dx.doi.org/10.1007/978-3-642-13287-23
F. Zhang, Z. Zheng, Y. Xiang, N. J. Yuan, X. Xie, and Z. Li ”DRN: A Deep Reinforcement LearningFramework for News Recommendation”, Proceedings of the World Wide Web Conference, 167-176, 2018.DOI: http://dx.doi.org/10.1145/3178876.3185994
W.-C. Kang, C. Fang, Z. Wang, and J. McAuley, ”Visually-aware fashion recommendation and designwith generative image models”, IEEE International Conference on Data Mining, 207-216, 2017.
V. C. S. Heung, H. Qu, and R. Chu, ”The relationship between vacation factors and socio-demographicand travelling characteristics: The case of Japanese leisure travellers”,Tourism Management22(3):259-269, 2001.
J. Bobadilla, F. Ortega, A. Hernando, and A. Guti ́errez, ”Recommender systems survey”,Knowledge-Based Systems46(2):109-132, 2013. DOI: http://dx.doi.org/10.1016/j.knosys.2013.03.012
M. Sharma and S. Mann, ”A Survey of Recommender Systems: Approaches and Limitations”,Interna-tional Journal of Innovations in Engineering and Technology2(2):8-14, 2013.
F. Mansur, V. Patel, and M. Patel, ”A review on recommender systems”,IEEE International Con-ference on Innovations in Information, Embedded and Communication Systems, 1-6, 2017. DOI:http://dx.doi.org/10.1109/ICIIECS.2017.8276182
J. Salter and N. Antonopoulos, ”CinemaScreen Recommender Agent:Combining Collaborative andContent-Based Filtering”,IEEE Intelligent Systems21(1):35-41, 2006
H. Li, F. Cai, and Z. Liao, ”Content-based filtering recommendation algorithm using HMM”,IEEE FourthInternational Conference on Computational and Information Sciences, 275-277, 2012
A. Patel, A. Thakkar, N. Bhatt, and P. Prajapati,Survey and Evolution Study Focusing Comparative Analy-sis and Future Research Direction in the Field of Recommendation System Specific to Collaborative Filter-ing Approach, Springer, Singapore, 155-163, 2019. DOI: http://dx.doi.org/10.1007/978-981-13-1742-216
J. Son and S. B. Kim, ”Content-based filtering for recommendation systems using multiattribute net-works”,Expert Systems with Applications89:404-412, 2017.
Y. B. Fernandez, J. J. P. Aris, A. G. Dolla, M. R. Cabrer, and M. L. Nores, ”Providing Entertainment byContent-based Filtering and Semantic Reasoning in Intelligent Recommender Systems”,IEEE Transac-tions on Consumer Electronics54(2):727-735, 2008.
D. Goldberg,D. Nichols,B. M. Oki,and D. Terry,”Using collaborative filtering toWeave an Information tapestry”, Communications of the ACM35(12):61-70, 1992. DOI:http://dx.doi.org/10.1145/138859.138867
S. Khatwani and D. M. B. Chandak, ”Building Personalized and Non Personalized RecommendationSystems”,IEEE International Conference on Automatic Control and Dynamic Optimization Techniques,623-628, 2016. DOI: http://dx.doi.org/10.1109/ICACDOT.2016.7877661
C. M. Wu, D. Garg, and U. Bhandary, ”Movie Recommendation System Using Collaborative Filtering”,IEEE 9th International Conference on Software Engineering and Service Science, 11-15, 2018.
M. Nilashi, O. Ibrahim, and K. Bagherifard, ”A recommender system based on collaborative filteringusing ontology and dimensionality reduction techniques”,Expert Systems with Applications92:507-520,2018. DOI: http://dx.doi.org/10.1016/j.eswa.2017.09.058
T. Zhou, L. Chen, and J. Shen, ”Movie Recommendation System Employing the User-Based CF in CloudComputing”,IEEE International Conference on Computational Science and Engineering and IEEE Inter-national Conference on Embedded and Ubiquitous Computing, 2:46-50, 2017.
F.O. Isinkaye,Y. O. Folajimi,and B. A. Ojokoh, ”Recommendation systems: Principles, methods and evaluation”, Egyptian Informatics Journal16(3):261-273, 2015. DOI:http://dx.doi.org/10.1016/j.eij.2015.06.005
T. Bai, J. R. Wen, J. Zhang, and W. X. Zhao, ”A neural collaborative filtering model with interaction-based neighborhood”, Proceedings of ACM on Conference on Information and Knowledge Management,1979-1982, 2017.
L. T. Ponnam, S. D. Punyasamudram, S. N. Nallagulla, and S. Yellamati, ”Movie recommender systemusing item based collaborative filtering technique”, IEEE International Conference on Emerging Trends inEngineering, Technology and Science, 1-5, 2016
J. Bobadilla,F. Serradilla,and A. Hernando,”Collaborative filtering adapted to rec-ommendersystemsofe-learning”,Knowledge-BasedSystems22(4):261-265, 2009.DOI:http://dx.doi.org/10.1016/j.knosys.2009.01.008
Xiaoyuan Su and T. M. Khoshgoftaar, ”A Survey of Collaborative Filtering Techniques”,Advances inartificial intelligence2009:1-9, 2009.
Delgado, Joaquin, and N. Ishii, ”Memory-Based Weighted-Majority Prediction for Recommender Sys-tems”,Res. Dev. Inf. Retr1999.
J. Pareek, M. Jhaveri, A. Kapasi, and M. Trivedi,”SNetRS: Social networking in Recommendation sys-tem”, Springer, Berlin, Heidelberg, 195-206, 2013. DOI: http://dx.doi.org/10.1007/978-3-642-31552-721
M. Deshpande and G. Karypis, ”Item-based Top-N Recommendation Algorithms”,ACM Transactions onInformation Systems22(1):143-177, 2004. DOI: http://dx.doi.org/10.1145/963770.963776
D. Almazro, G. Shahatah, L. Albdulkarim, M. Kherees, R. Martinez, and W. Nzoukou, ”A Survey Paperon Recommender Systems”,arXiv preprint arXiv:1006.52782010
H. Koohi and K. Kiani, ”User based Collaborative Filtering using fuzzy C-means”,Measurement: Journalof the International Measurement Confederation91:134-139,2016.
N. Yi, C. Li, M. Shi, and X. Feng, ”Design and Implementation of Movie Recommender System Basedon Graph Database”,IEEE 14th Web Information Systems and Applications Conference, 132-135, 2017
C.-H. Piao, J. Zhao, and L. J. Zheng, ”Research on entropy-based collaborative filtering algorithm andpersonalized recommendation in e-commerce”,Service Oriented Computing and Applications3(2):147-157, 2009.
G. Badaro, H. Hajj, W. El-Hajj, and L. Nachman, ”A Hybrid Approach with Collaborative Filteringfor Recommender Systems”,IEEE 9th International Wireless Communications and Mobile ComputingConference, 349-354, 2013. DOI: http://dx.doi.org/10.1109/IWCMC.2013.6583584
H. Liu, Z. Hu, A. Mian, H. Tian, and X. Zhu, ”A new user similarity model to improve the accuracy ofcollaborative filtering”,Knowledge-Based Systems56:156-166, 2014
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, ”Item-based collaborative filtering recommendation al-gorithms”,Proceedings of the 10th International Conference on World Wide Web, 285-295, 2001. DOI:http://dx.doi.org/10.1145/371920.372071
M. Liphoto, C. Du, and S. Ngwira, ”A Survey on Recommender Systems”,IEEE InternationalConference on Advances in Computing and Communication Engineering, 276-280, 2016. DOI:http://dx.doi.org/10.1109/ICACCE.2016.8073761
A. K. S. C. Pradhan and B. S. P. Mishra, ”SVD based Privacy Preserving Recommendation Model usingOptimized Hybrid Item-based Collaborative Filtering”,IEEE International Conference on Communicationand Signal Processing, 0294-0298, 2019.
Y. Koren, R. Bell, and C. Volinsky, ”Matrix Factorization Techniques For Recommender Systems”,Com-puter42(8):30-37, 2009. DOI: http://dx.doi.org/10.1109/MC.2009.263
G. Takacs, I. Pilaszy, B. Nemeth, and D. Tikk, ”Major components of the Gravity RecommendationSystem”,Acm Sigkdd Explorations Newsletter9(2):80-83, 2007.
B. Abdollahi and O. Nasraoui, ”Explainable Matrix Factorization for Collaborative Filtering”, Proceedings of the 25th International Conference Companion on World Wide Web, 5-6, 2016. DOI:http://dx.doi.org/10.1145/2872518.2889405
D. Bokde, S. Girase, and D. Mukhopadhyay, ”Matrix Factorization model in Collabora-tive Filtering algorithms: A survey”, Procedia Computer Science49:136-146, 2015. DOI:http://dx.doi.org/10.1016/j.procs.2015.04.237
Y. Azar, A. Fiat, A. Karlin, F. McSherry, and J. Saia, ”Spectral analysis of data”,Conference Proceedings of the Annual ACM Symposium on Theory of Computing, 619-626, 2001. DOI:http://dx.doi.org/10.1145/380752.380859
M. E. Wall, A. Rechtsteiner, and L. M. Rocha,”Singular Value Decomposition and Principal ComponentAnalysis”, Springer, Boston, MA, 91-109, 2005.
M. G. Vozalis and K. G. Margaritis, ”Using SVD and demographic data for the enhance-ment of generalized Collaborative Filtering”,Information Sciences177(15):3017-3037, 2007. DOI:http://dx.doi.org/10.1016/j.ins.2007.02.036
K. N. Jain, V. Kumar, P. Kumar, and T. Choudhury,”Movie recommendation system: Hybrid informationFiltering System”, Springer, Singapore, 677-686, 2018
S. M. Ali, G. K. Nayak, R. K. Lenka, and R. K. Barik,”Movie Recommendation System Using GenomeTags and Content-Based Filtering”, Springer, Singapore, 85-94, 2018.
F. Deng, P. Ren, Z. Qin, G. Huang, and Z. Qin, ”Leveraging Image Visual Features in Content-Based Rec-ommender System”,Scientific Programming 2018, 2018. DOI: http://dx.doi.org/10.1155/2018/5497070
C. Yang, X. Chen, L. Liu, T. Liu, and S. Geng, ”A Hybrid Movie Recommendation Method Based onSocial Similarity and Item Attributes”,International Conference on Sensing and Imaging. Springer, Cham, 275-285, 2018
Priscila Valdiviezo D ́ıaz and J. Bobadilla, ”A Hybrid Approach of Recommendation via Extended MatrixBased on Collaborative Filtering with Demographics Information”,International Conference on Technol-ogy Trends. Springer, Cham, 384-398, 2018.
R. Bharti and D. Gupta,”Recommending Top N Movies Using Content-Based Filtering and Col-laborative Filtering with Hadoop and Hive Framework”, Springer, Singapore, 109-118, 2019. DOI:http://dx.doi.org/10.1007/978-981-13-1280-910
M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin, ”Combining Content-Basedand collaborative filters in an Online Newspaper”,Proceedings of ACM SIGIR Workshop on RecommenderSystems, 1999.
M. Braunhofer, V. Codina, and F. Ricci, ”Switching hybrid for cold-starting context-aware recommendersystems”,Proceedings of the 8th ACM Conference on Recommender systems, 349-352, 2014. DOI:http://dx.doi.org/10.1145/2645710.2645757
A. S. Lampropoulos, P. S. Lampropoulou, and G. A. Tsihrintzis, ”A Cascade-Hybrid Music RecommenderSystem for mobile services based on musical genre classification and personality diagnosis”,MultimediaTools and Applications59(1):241-258, 2012. DOI: http://dx.doi.org/10.1007/s11042-011-0742-0
A. M and A. Wasfi, ”Collecting user access patterns for building user profiles and collaborative filtering”,Proceedings of the 4th international conference on Intelligent user interfaces, 57-64, 1998.
E. Cano and M. Morisio, ”Hybrid Recommender Systems: A Systematic Literature Review”,IntelligentData Analysis21(6):1487-1524, 2017.
R. Burke,”Hybrid Systems for Personalized Recommendations”, Springer, Berlin, Heidelberg, 133-152,2003. DOI: http://dx.doi.org/10.1007/115779357
J. Lund and Y. Ng, ”Movie Recommendations Using the Deep Learning Approach”,IEEE Inter-national Conference on Information Reuse and Integration, 47-54, Salt Lake City, UT, 2018. DOI:http://dx.doi.org/10.1109/IRI.2018.00015
Kumar, Sudhanshu, Kanjar De, and Partha Pratim Roy, ”Movie recommendation system using sentimentanalysis from microblogging data”,IEEE Transactions on Computational Social Systems2020.
Leung, John Kalung, Igor Griva, and William G. Kennedy, ”Making Use of Affective Features from MediaContent Metadata for Better Movie Recommendation Making”, arXiv preprint arXiv:2007.00636 (2020
Singh, Tarana, Anand Nayyar, and Arun Solanki, ”Multilingual Opinion Mining Movie RecommendationSystem Using RNN”,First International Conference on Computing, Communications, and Cyber-Security,Springer, Singapore, 2020.
Bobadilla, Jesus, Santiago Alonso, and Antonio Hernando, ”Deep Learning Architecture forCollaborative Filtering Recommender Systems”, Applied Sciences10(7):24-41,2020. DOI:http://dx.doi.org/10.3390/app10072441
M. Ayub, M. A. Ghazanfar, M. Maqsood, and A. Saleem, ”A Jaccard base similarity measure to im-prove performance of CF based recommender systems”,IEEE International Conference on InformationNetworking, 1-6, 2018.
T. Q. Lee, Y. Park, and Y. T. Park, ”A similarity measure for collaborative filtering with implicit feedback”,International Conference on Intelligent Computing. Springer, Berlin, Heidelberg, 385-397, 2007
S. Bansal, C. Gupta, and A. Arora, ”User tweets based genre prediction and movie recommendation usingLSI and SVD”,IEEE Ninth International Conference on Contemporary Computing, 1-6, 2016. DOI:http://dx.doi.org/10.1109/IC3.2016.7880220
S. M. Choi, S. K. Ko, and Y. S. Han, ”A movie recommendation algorithm based on genre correlations”,Expert Systems with Applications39(9):8079-8085, 2012
S. Bag, S. K. Kumar, and M. K. Tiwari, ”An efficient recommendation generation using relevant Jaccardsimilarity”,Information Sciences483:53-64, 2019
G. Shani and A. Gunawardana,”Evaluating Recommendation Systems”, Springer, Boston, MA, 257-297,2011. DOI: http://dx.doi.org/10.1007/978-0-387-85820-38
D. Parra and S. Sahebi,”Recommender systems: Sources of knowledge and evaluation metrics”, Springer,Berlin, Heidelberg, 149-175, 2013. DOI:http://dx.doi.org/10.1007/978-3-642-33326-27
G. Carullo, A. Castiglione, A. De Santis, and F. Palmieri, ”A triadic closure and homophily-basedrecommendation system for online social networks”,World Wide Web18(6):1579-1601, 2015. DOI:http://dx.doi.org/10.1007/s11280-015-0333-5
G. Adomavicius and Y. Kwon, ”Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques”,IEEE Transactions on Knowledge and Data Engineering24(5):896-911, 2011. DOI:http://dx.doi.org/10.1109/TKDE.2011.15
G. Adomavicius and Y. Kwon,”Optimization-based approaches for maximizing aggregaterecommendationdiversity”,INFORMSJournalonComputing26(2):351-369,2014.DOI:http://dx.doi.org/10.1287/ijoc.2013.0570
M. F. Aljunid and D. H. Manjaiah,Movie Recommender System Based on Collaborative Filtering UsingApache Spark, Springer, Singapore, 283-295, 2019. DOI: http://dx.doi.org/10.1007/978-981-13-1274-822
R. Katarya and O. P. Verma, ”An effective collaborative movie recommender system with cuckoo search”,Egyptian Informatics Journal18(2):105-112, 2017. DOI: http://dx.doi.org/10.1016/j.eij.2016.10.002
S. H. Min and I. Han, ”Detection of the customer time-variant pattern for improving recommender sys-tems”,Expert Systems with Applications28(2):189-199, 2005.
S. Wattal, Y. Hong, M. Mandviwalla, and A. Jain, ”Technology diffusion in the society: Analyzing digitaldivide in the context of social class”,IEEE Proc. of 44th Hawaii International Conference on SystemSciences, 1-10, 2011. DOI: http://dx.doi.org/10.1109/HICSS.2011.398
M. Goldmann and G. Kreitz, ”Measurements on the spotify peer-assisted music-on-demand stream-ing system”,IEEE International Conference on Peer-to-Peer Computing, 206-211, 2011. DOI:http://dx.doi.org/10.1109/P2P.2011.6038737
P. N. V. Kumar and V. R. Reddy, ”A Survey on Recommender Systems (RSS) and Its Applications”,International Journal of Innovative Research in Computer and Communication Engineering2(8):5254-5260, 2014.
C. A. Gomez-Uribe and N. Hunt, ”The Netflix Recommender System: Algorithms, Business Value,and Innovation”, ACM Transactions on Management Information Systems6(4):1-19, 2015. DOI:http://dx.doi.org/10.1145/2843948
O.Celma,MusicRecommendation,Springer,Berlin,Heidelberg,43-85,2010.DOI:http://dx.doi.org/10.1007/978-3-642-13287-23
F. Zhang, Z. Zheng, Y. Xiang, N. J. Yuan, X. Xie, and Z. Li ”DRN: A Deep Reinforcement LearningFramework for News Recommendation”, Proceedings of the World Wide Web Conference, 167-176, 2018.DOI: http://dx.doi.org/10.1145/3178876.3185994
W.-C. Kang, C. Fang, Z. Wang, and J. McAuley, ”Visually-aware fashion recommendation and designwith generative image models”, IEEE International Conference on Data Mining, 207-216, 2017.
V. C. S. Heung, H. Qu, and R. Chu, ”The relationship between vacation factors and socio-demographicand travelling characteristics: The case of Japanese leisure travellers”,Tourism Management22(3):259-269, 2001.
J. Bobadilla, F. Ortega, A. Hernando, and A. Guti ́errez, ”Recommender systems survey”,Knowledge-Based Systems46(2):109-132, 2013. DOI: http://dx.doi.org/10.1016/j.knosys.2013.03.012
M. Sharma and S. Mann, ”A Survey of Recommender Systems: Approaches and Limitations”,Interna-tional Journal of Innovations in Engineering and Technology2(2):8-14, 2013.
F. Mansur, V. Patel, and M. Patel, ”A review on recommender systems”,IEEE International Con-ference on Innovations in Information, Embedded and Communication Systems, 1-6, 2017. DOI:http://dx.doi.org/10.1109/ICIIECS.2017.8276182
J. Salter and N. Antonopoulos, ”CinemaScreen Recommender Agent:Combining Collaborative andContent-Based Filtering”,IEEE Intelligent Systems21(1):35-41, 2006
H. Li, F. Cai, and Z. Liao, ”Content-based filtering recommendation algorithm using HMM”,IEEE FourthInternational Conference on Computational and Information Sciences, 275-277, 2012
A. Patel, A. Thakkar, N. Bhatt, and P. Prajapati,Survey and Evolution Study Focusing Comparative Analy-sis and Future Research Direction in the Field of Recommendation System Specific to Collaborative Filter-ing Approach, Springer, Singapore, 155-163, 2019. DOI: http://dx.doi.org/10.1007/978-981-13-1742-216
J. Son and S. B. Kim, ”Content-based filtering for recommendation systems using multiattribute net-works”,Expert Systems with Applications89:404-412, 2017.
Y. B. Fernandez, J. J. P. Aris, A. G. Dolla, M. R. Cabrer, and M. L. Nores, ”Providing Entertainment byContent-based Filtering and Semantic Reasoning in Intelligent Recommender Systems”,IEEE Transac-tions on Consumer Electronics54(2):727-735, 2008.
D. Goldberg,D. Nichols,B. M. Oki,and D. Terry,”Using collaborative filtering toWeave an Information tapestry”, Communications of the ACM35(12):61-70, 1992. DOI:http://dx.doi.org/10.1145/138859.138867
S. Khatwani and D. M. B. Chandak, ”Building Personalized and Non Personalized RecommendationSystems”,IEEE International Conference on Automatic Control and Dynamic Optimization Techniques,623-628, 2016. DOI: http://dx.doi.org/10.1109/ICACDOT.2016.7877661
C. M. Wu, D. Garg, and U. Bhandary, ”Movie Recommendation System Using Collaborative Filtering”,IEEE 9th International Conference on Software Engineering and Service Science, 11-15, 2018.
M. Nilashi, O. Ibrahim, and K. Bagherifard, ”A recommender system based on collaborative filteringusing ontology and dimensionality reduction techniques”,Expert Systems with Applications92:507-520,2018. DOI: http://dx.doi.org/10.1016/j.eswa.2017.09.058
T. Zhou, L. Chen, and J. Shen, ”Movie Recommendation System Employing the User-Based CF in CloudComputing”,IEEE International Conference on Computational Science and Engineering and IEEE Inter-national Conference on Embedded and Ubiquitous Computing, 2:46-50, 2017.
F.O. Isinkaye,Y. O. Folajimi,and B. A. Ojokoh, ”Recommendation systems: Principles, methods and evaluation”, Egyptian Informatics Journal16(3):261-273, 2015. DOI:http://dx.doi.org/10.1016/j.eij.2015.06.005
T. Bai, J. R. Wen, J. Zhang, and W. X. Zhao, ”A neural collaborative filtering model with interaction-based neighborhood”, Proceedings of ACM on Conference on Information and Knowledge Management,1979-1982, 2017.
L. T. Ponnam, S. D. Punyasamudram, S. N. Nallagulla, and S. Yellamati, ”Movie recommender systemusing item based collaborative filtering technique”, IEEE International Conference on Emerging Trends inEngineering, Technology and Science, 1-5, 2016
J. Bobadilla,F. Serradilla,and A. Hernando,”Collaborative filtering adapted to rec-ommendersystemsofe-learning”,Knowledge-BasedSystems22(4):261-265, 2009.DOI:http://dx.doi.org/10.1016/j.knosys.2009.01.008
Xiaoyuan Su and T. M. Khoshgoftaar, ”A Survey of Collaborative Filtering Techniques”,Advances inartificial intelligence2009:1-9, 2009.
Delgado, Joaquin, and N. Ishii, ”Memory-Based Weighted-Majority Prediction for Recommender Sys-tems”,Res. Dev. Inf. Retr1999.
J. Pareek, M. Jhaveri, A. Kapasi, and M. Trivedi,”SNetRS: Social networking in Recommendation sys-tem”, Springer, Berlin, Heidelberg, 195-206, 2013. DOI: http://dx.doi.org/10.1007/978-3-642-31552-721
M. Deshpande and G. Karypis, ”Item-based Top-N Recommendation Algorithms”,ACM Transactions onInformation Systems22(1):143-177, 2004. DOI: http://dx.doi.org/10.1145/963770.963776
D. Almazro, G. Shahatah, L. Albdulkarim, M. Kherees, R. Martinez, and W. Nzoukou, ”A Survey Paperon Recommender Systems”,arXiv preprint arXiv:1006.52782010
H. Koohi and K. Kiani, ”User based Collaborative Filtering using fuzzy C-means”,Measurement: Journalof the International Measurement Confederation91:134-139,2016.
N. Yi, C. Li, M. Shi, and X. Feng, ”Design and Implementation of Movie Recommender System Basedon Graph Database”,IEEE 14th Web Information Systems and Applications Conference, 132-135, 2017
C.-H. Piao, J. Zhao, and L. J. Zheng, ”Research on entropy-based collaborative filtering algorithm andpersonalized recommendation in e-commerce”,Service Oriented Computing and Applications3(2):147-157, 2009.
G. Badaro, H. Hajj, W. El-Hajj, and L. Nachman, ”A Hybrid Approach with Collaborative Filteringfor Recommender Systems”,IEEE 9th International Wireless Communications and Mobile ComputingConference, 349-354, 2013. DOI: http://dx.doi.org/10.1109/IWCMC.2013.6583584
H. Liu, Z. Hu, A. Mian, H. Tian, and X. Zhu, ”A new user similarity model to improve the accuracy ofcollaborative filtering”,Knowledge-Based Systems56:156-166, 2014
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, ”Item-based collaborative filtering recommendation al-gorithms”,Proceedings of the 10th International Conference on World Wide Web, 285-295, 2001. DOI:http://dx.doi.org/10.1145/371920.372071
M. Liphoto, C. Du, and S. Ngwira, ”A Survey on Recommender Systems”,IEEE InternationalConference on Advances in Computing and Communication Engineering, 276-280, 2016. DOI:http://dx.doi.org/10.1109/ICACCE.2016.8073761
A. K. S. C. Pradhan and B. S. P. Mishra, ”SVD based Privacy Preserving Recommendation Model usingOptimized Hybrid Item-based Collaborative Filtering”,IEEE International Conference on Communicationand Signal Processing, 0294-0298, 2019.
Y. Koren, R. Bell, and C. Volinsky, ”Matrix Factorization Techniques For Recommender Systems”,Com-puter42(8):30-37, 2009. DOI: http://dx.doi.org/10.1109/MC.2009.263
G. Takacs, I. Pilaszy, B. Nemeth, and D. Tikk, ”Major components of the Gravity RecommendationSystem”,Acm Sigkdd Explorations Newsletter9(2):80-83, 2007.
B. Abdollahi and O. Nasraoui, ”Explainable Matrix Factorization for Collaborative Filtering”, Proceedings of the 25th International Conference Companion on World Wide Web, 5-6, 2016. DOI:http://dx.doi.org/10.1145/2872518.2889405
D. Bokde, S. Girase, and D. Mukhopadhyay, ”Matrix Factorization model in Collabora-tive Filtering algorithms: A survey”, Procedia Computer Science49:136-146, 2015. DOI:http://dx.doi.org/10.1016/j.procs.2015.04.237
Y. Azar, A. Fiat, A. Karlin, F. McSherry, and J. Saia, ”Spectral analysis of data”,Conference Proceedings of the Annual ACM Symposium on Theory of Computing, 619-626, 2001. DOI:http://dx.doi.org/10.1145/380752.380859
M. E. Wall, A. Rechtsteiner, and L. M. Rocha,”Singular Value Decomposition and Principal ComponentAnalysis”, Springer, Boston, MA, 91-109, 2005.
M. G. Vozalis and K. G. Margaritis, ”Using SVD and demographic data for the enhance-ment of generalized Collaborative Filtering”,Information Sciences177(15):3017-3037, 2007. DOI:http://dx.doi.org/10.1016/j.ins.2007.02.036
K. N. Jain, V. Kumar, P. Kumar, and T. Choudhury,”Movie recommendation system: Hybrid informationFiltering System”, Springer, Singapore, 677-686, 2018
S. M. Ali, G. K. Nayak, R. K. Lenka, and R. K. Barik,”Movie Recommendation System Using GenomeTags and Content-Based Filtering”, Springer, Singapore, 85-94, 2018.
F. Deng, P. Ren, Z. Qin, G. Huang, and Z. Qin, ”Leveraging Image Visual Features in Content-Based Rec-ommender System”,Scientific Programming 2018, 2018. DOI: http://dx.doi.org/10.1155/2018/5497070
C. Yang, X. Chen, L. Liu, T. Liu, and S. Geng, ”A Hybrid Movie Recommendation Method Based onSocial Similarity and Item Attributes”,International Conference on Sensing and Imaging. Springer, Cham, 275-285, 2018
Priscila Valdiviezo D ́ıaz and J. Bobadilla, ”A Hybrid Approach of Recommendation via Extended MatrixBased on Collaborative Filtering with Demographics Information”,International Conference on Technol-ogy Trends. Springer, Cham, 384-398, 2018.
R. Bharti and D. Gupta,”Recommending Top N Movies Using Content-Based Filtering and Col-laborative Filtering with Hadoop and Hive Framework”, Springer, Singapore, 109-118, 2019. DOI:http://dx.doi.org/10.1007/978-981-13-1280-910
M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin, ”Combining Content-Basedand collaborative filters in an Online Newspaper”,Proceedings of ACM SIGIR Workshop on RecommenderSystems, 1999.
M. Braunhofer, V. Codina, and F. Ricci, ”Switching hybrid for cold-starting context-aware recommendersystems”,Proceedings of the 8th ACM Conference on Recommender systems, 349-352, 2014. DOI:http://dx.doi.org/10.1145/2645710.2645757
A. S. Lampropoulos, P. S. Lampropoulou, and G. A. Tsihrintzis, ”A Cascade-Hybrid Music RecommenderSystem for mobile services based on musical genre classification and personality diagnosis”,MultimediaTools and Applications59(1):241-258, 2012. DOI: http://dx.doi.org/10.1007/s11042-011-0742-0
A. M and A. Wasfi, ”Collecting user access patterns for building user profiles and collaborative filtering”,Proceedings of the 4th international conference on Intelligent user interfaces, 57-64, 1998.
E. Cano and M. Morisio, ”Hybrid Recommender Systems: A Systematic Literature Review”,IntelligentData Analysis21(6):1487-1524, 2017.
R. Burke,”Hybrid Systems for Personalized Recommendations”, Springer, Berlin, Heidelberg, 133-152,2003. DOI: http://dx.doi.org/10.1007/115779357
J. Lund and Y. Ng, ”Movie Recommendations Using the Deep Learning Approach”,IEEE Inter-national Conference on Information Reuse and Integration, 47-54, Salt Lake City, UT, 2018. DOI:http://dx.doi.org/10.1109/IRI.2018.00015
Kumar, Sudhanshu, Kanjar De, and Partha Pratim Roy, ”Movie recommendation system using sentimentanalysis from microblogging data”,IEEE Transactions on Computational Social Systems2020.
Leung, John Kalung, Igor Griva, and William G. Kennedy, ”Making Use of Affective Features from MediaContent Metadata for Better Movie Recommendation Making”, arXiv preprint arXiv:2007.00636 (2020
Singh, Tarana, Anand Nayyar, and Arun Solanki, ”Multilingual Opinion Mining Movie RecommendationSystem Using RNN”,First International Conference on Computing, Communications, and Cyber-Security,Springer, Singapore, 2020.
Bobadilla, Jesus, Santiago Alonso, and Antonio Hernando, ”Deep Learning Architecture forCollaborative Filtering Recommender Systems”, Applied Sciences10(7):24-41,2020. DOI:http://dx.doi.org/10.3390/app10072441
M. Ayub, M. A. Ghazanfar, M. Maqsood, and A. Saleem, ”A Jaccard base similarity measure to im-prove performance of CF based recommender systems”,IEEE International Conference on InformationNetworking, 1-6, 2018.
T. Q. Lee, Y. Park, and Y. T. Park, ”A similarity measure for collaborative filtering with implicit feedback”,International Conference on Intelligent Computing. Springer, Berlin, Heidelberg, 385-397, 2007
S. Bansal, C. Gupta, and A. Arora, ”User tweets based genre prediction and movie recommendation usingLSI and SVD”,IEEE Ninth International Conference on Contemporary Computing, 1-6, 2016. DOI:http://dx.doi.org/10.1109/IC3.2016.7880220
S. M. Choi, S. K. Ko, and Y. S. Han, ”A movie recommendation algorithm based on genre correlations”,Expert Systems with Applications39(9):8079-8085, 2012
S. Bag, S. K. Kumar, and M. K. Tiwari, ”An efficient recommendation generation using relevant Jaccardsimilarity”,Information Sciences483:53-64, 2019
G. Shani and A. Gunawardana,”Evaluating Recommendation Systems”, Springer, Boston, MA, 257-297,2011. DOI: http://dx.doi.org/10.1007/978-0-387-85820-38
D. Parra and S. Sahebi,”Recommender systems: Sources of knowledge and evaluation metrics”, Springer,Berlin, Heidelberg, 149-175, 2013. DOI:http://dx.doi.org/10.1007/978-3-642-33326-27
G. Carullo, A. Castiglione, A. De Santis, and F. Palmieri, ”A triadic closure and homophily-basedrecommendation system for online social networks”,World Wide Web18(6):1579-1601, 2015. DOI:http://dx.doi.org/10.1007/s11280-015-0333-5
G. Adomavicius and Y. Kwon, ”Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques”,IEEE Transactions on Knowledge and Data Engineering24(5):896-911, 2011. DOI:http://dx.doi.org/10.1109/TKDE.2011.15
G. Adomavicius and Y. Kwon,”Optimization-based approaches for maximizing aggregaterecommendationdiversity”,INFORMSJournalonComputing26(2):351-369,2014.DOI:http://dx.doi.org/10.1287/ijoc.2013.0570
M. F. Aljunid and D. H. Manjaiah,Movie Recommender System Based on Collaborative Filtering UsingApache Spark, Springer, Singapore, 283-295, 2019. DOI: http://dx.doi.org/10.1007/978-981-13-1274-822