Review on Recommender System and Architecture
Subject Areas : Majlesi Journal of Telecommunication Devices
1 - Department of Engineering, Isfahan Branch, Islamic Azad University, Iran
Keywords: recommender system, Evolutionary computing, Electronic Commerce, intelligent system,
Abstract :
Today, global information societies are increasingly producing a mass of information, which makes it difficult to access relevant and useful information at the moment. In the meantime, there are many services and products that need to be filtered and presented based on the priorities of users. Recommender systems emerged as a tool to deal with the mass of data to respond to the existing need. These systems collect user information or information that helps users to provide a list of items explicitly or implicitly to suggest to users. With the flourishing of electronic commerce, the use of recommender systems in various aspects of online business has revolutionized electronic commerce
References:
[1] Wang, Y. Liang, D. Xu, X. Feng, and R. Guan, "A content-based recommender system for computer science publications," Knowledge-Based Systems, vol. 157, pp. 1-9, 2018.
[2] Isinkaye, Y. Folajimi, and B. Ojokoh, "Recommendation systems: Principles, methods and evaluation," Egyptian Informatics Journal, vol. 16, no. 3, pp. 261-273, 2015.
[3] Wasid and R. Ali, "An improved recommender system based on multi-criteria clustering approach," Procedia Computer Science, vol. 131, pp. 93-101, 2018.
[4] L. Peška, T. M. Tashu, and T. Horváth, "Swarm intelligence techniques in recommender systems-A review of recent research," Swarm and Evolutionary Computation, 48, pp. 201-219, 2019.
[5] P. Singh and S. Solanki, "Recommender System Survey: Clustering to Nature Inspired Algorithm," in Proceedings of 2nd International Conference on Communication, Computing and Networking, 2019, pp. 757-768: Springer.
[6] Silveira, M. Zhang, X. Lin, Y. Liu, and S. Ma, "How good your recommender system is? A survey on evaluations in recommendation," International Journal of Machine Learning and Cybernetics, vol. 10, no. 5, pp. 813-831, 2019.
[7] Gupta and S. Goel, "Handling user cold start problem in recommender systems using fuzzy clustering," in Information and Communication Technology for Sustainable Development: Springer, 2018, pp. 143-151.
[8] Mohammadpour, A. M. Bidgoli, R. Enayatifar, and H. H. S. Javadi, "Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm," Genomics, vol. 111, no. 6, pp. 1902-1912, 2019.
[9] Sheta, H. Faris, M. Braik, and S. Mirjalili, "Nature-Inspired Metaheuristics Search Algorithms for Solving the Economic Load Dispatch Problem of Power," Applied Nature-Inspired Computing: Algorithms and Case Studies, p. 199, 2019.
[10] V. Altay and B. Alatas, "Performance comparisons of socially inspired metaheuristic algorithms on unconstrained global optimization," in Advances in Computer Communication and Computational Sciences: Springer, 2019, pp. 163-175.
[11] Xinchang, P. Vilakone, and D.-S. Park, "Movie Recommendation Algorithm Using Social Network Analysis to Alleviate Cold-Start Problem," Journal of Information Processing Systems, vol. 15, no. 3, 2019.
[12] Revathy and S. P. Anitha, "Cold Start Problem in Social Recommender Systems: State-of-the-Art Review," in Advances in Computer Communication and Computational Sciences: Springer, 2019, pp. 105-115.
[13] H. Greer, "Critical success factors in developing, implementing, and teaching a Web development course," Journal of Information Systems Education, vol. 12, no. 3, p. 5, 2020.
[14] Abdullah, R. Ramli, H. Bakodah, and M. Othman, "Developing a causal relationship among factors of e-commerce: a decision making approach," Journal of King Saud University-Computer and Information Sciences, 2019.
[15] Varga, "Recommender Systems," in Practical Data Science with Python 3: Springer, 2019, pp. 317-339.
[16] Najmani, B. El habib, N. Sael, and A. Zellou, "A Comparative Study on Recommender Systems Approaches," in Proceedings of the 4th International Conference on Big Data and Internet of Things, 2019, pp. 1-5.
[17] Ojokoh, M. G. Asogbon, O. W. Samuel, and B. S. Adeniyi, "Fuzzy Driven Decision Support System for Enhanced Employee Performance Appraisal," International Journal of Human Capital and Information Technology Professionals (IJHCITP), vol. 11, no. 1, pp. 17-30, 2020.
[18] Milano, M. Taddeo, and L. Floridi, "Recommender systems and their ethical challenges," AI & SOCIETY, pp. 1-11, 2020.
[19] Samih, A. Adadi, and M. Berrada, "Towards a knowledge based Explainable Recommender Systems," in Proceedings of the 4th International Conference on Big Data and Internet of Things, 2019, pp. 1-5.
[20] Shokeen and C. Rana, "A study on features of social recommender systems," Artificial Intelligence Review, vol. 53, no. 2, pp. 965-988, 2020.
[21] Ricci, L. Rokach, and B. Shapira, "Recommender systems: introduction and challenges," in Recommender systems handbook: Springer, 2015, pp. 1-34.
[22] Ricci, L. Rokach, and B. Shapira, "Introduction to recommender systems handbook," in Recommender systems handbook: Springer, 2011, pp. 1-35.
[23] H. Son, "HU-FCF: a hybrid user-based fuzzy collaborative filtering method in recommender systems," Expert Systems with Applications: An International Journal, vol. 41, no. 15, pp. 6861-6870, 2014.
[24] Bernardis, M. Ferrari Dacrema, and P. Cremonesi, "Estimating Confidence of Individual User Predictions in Item-based Recommender Systems," in Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, 2019, pp. 149-156.
[25] Valcarce, A. Landin, J. Parapar, and Á. Barreiro, "Collaborative filtering embeddings for memory-based recommender systems," Engineering Applications of Artificial Intelligence, vol. 85, pp. 347-356, 2019.
[26] Sun and Y. Xu, "Topic Model-Based Recommender System for Longtailed Products Against Popularity Bias," in 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC), 2019, pp. 250-256: IEEE.
[27] Sánchez-Corcuera, D. Casado-Mansilla, C. E. Borges, and D. López-de-Ipiña, "Persuasion-based recommender system ensambling matrix factorisation and active learning models," Personal and Ubiquitous Computing, pp. 1-11, 2020.
[28] Loboda, J. Nyhan, S. Mahony, D. M. Romano, and M. Terras, "Content-based Recommender Systems for Heritage: Developing a Personalised Museum Tour," 2019.
[29] Cano, "Rating aware feature selection in content-based recommender systems," 2019.
[30] Lops, D. Jannach, C. Musto, T. Bogers, and M. Koolen, "Trends in content-based recommendation," User Modeling and User-Adapted Interaction, vol. 29, no. 2, pp. 239-249, 2019.
[31] Beel, B. Gipp, S. Langer, and C. Breitinger, "paper recommender systems: a literature survey," International Journal on Digital Libraries, vol. 17, no. 4, pp. 305-338, 2016.
[32] Yago, J. Clemente, and D. Rodriguez, "Competence-based recommender systems: a systematic literature review," Behaviour & Information Technology, vol. 37, no. 10-11, pp. 958-977, 2018.
[33] K. A. Hassan and A. B. A. Abdulwahhab, "Reviews Sentiment analysis for collaborative recommender system," Kurdistan journal of applied research, vol. 2, no. 3, pp. 87-91, 2017.
[34] Anandhan, L. Shuib, M. A. Ismail, and G. Mujtaba, "Social media recommender systems: review and open research issues," IEEE Access, vol. 6, pp. 15608-15628, 2018.
[35] Hussain and S. Lee, "Addressing cold start problem through unfavorable reviews and specification of products in recommender system," in Proceedings of the Korea Information Processing Society Conference, 2017, pp. 914-915: Korea Information Processing Society.
[36] Vairachilai, M. Kavithadevi, and M. Raja, "Alleviating the cold start problem in recommender systems based on modularity maximization community detection algorithm," Circuits and Systems, vol. 7, no. 08, p. 1268, 2016.
[37] [37] Alhijawi and Y. Kilani, "The recommender system: A survey," International Journal of Advanced Intelligence Paradigms, vol. 15, no. 3, pp. 229-251, 2020.
[38] [38] Idrissi and A. Zellou, "A systematic literature review of sparsity issues in recommender systems," Social Network Analysis and Mining, vol. 10, no. 1, p. 15, 2020.
Fogelman‐Soulié et al., "Recommender Systems and Attributed Networks," Advances in Data Science: Symbolic, Complex and Network Data, vol. 4, pp. 139-167, 2020.
[39] Maazouzi, H. Zarzour, and Y. Jararweh, "An effective recommender system based on clustering technique for ted talks," International Journal of Information Technology and Web Engineering (IJITWE), vol. 15, no. 1, pp. 35-51, 2020.
[40] Jain, M. Murty, and P. Flynn, "Data Clustering: a review," 1996.
[41] Nayak, B. Naik, and H. Behera, "Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014," in Computational intelligence in data mining-volume 2: Springer, 2015, pp. 133-149.
[42] A. Kumar, M. Kumar, and H. Sheshadri, "Computer Aided Detection of Clustered Microcalcification: A Survey," Current Medical Imaging Reviews, vol. 15, no. 2, pp. 132-149, 2019.