Abstract
The Recommender system is a revolutionary technique in E-commerce marketing representing the user preferences and suggestions on items such as books, movies or products. Also, the preferences for any target users are predicted using different recommendation methods. The preferences for any target user can be tailored using different Recommendation methods, such as Content, Collaborative and Hybrid Techniques. There are different challenges that are addressed by the recommendation system: classifying and clustering the user preferences and ratings, finding the users who have common characteristics, suggesting the suitable items to the target user based on past history of users. This chapter provides an elaborative discussion on variety of Recommendation systems techniques, clustering techniques and their merits and demerits.
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Dhanalakshmi, P., Reddy, P.D.K., Gurumurthy, S., Lalitha, K. (2020). Web User Clustering Techniques for Recommendation Systems. In: Kumar, A., Paprzycki, M., Gunjan, V. (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_192
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DOI: https://doi.org/10.1007/978-981-15-1420-3_192
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