Abstract
An enormous amount of data available on the e-commerce sites are of different forms as ratings, reviews, opinions, remarks, feedback, and comments about any item, and it is difficult for the system to search the user interest and predict the user preference. The recommender system (RS) came into existence and supports both customers and providers in their decision-making process. Nowadays, recommender systems are suffering from various problems such as data sparsity, cold start, scalability, synonymy, gray sheep, and data imbalance. One of the major problems to be considered for better recommendation is data sparsity. Cross-domain recommendation (CDR) is one way to address data sparsity problems, cold start issues, etc. In the most traditional system, cross-domain analysis is used to understand the feedback matrices by transferring hidden information and imposing dependencies across the domains. There is no vast comparison of existing research in CDR. This paper defines the problem, related and existing work on CDR for data sparsity and cold start, comparative survey to classify and analyze the revised work.
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Nanthini, M., Pradeep Mohan Kumar, K. (2023). Cold start and Data Sparsity Problems in Recommender System: A Concise Review. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 473. Springer, Singapore. https://doi.org/10.1007/978-981-19-2821-5_9
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DOI: https://doi.org/10.1007/978-981-19-2821-5_9
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