Abstract
Modern E-Commerce sites require a concrete method of retaining their user base besides keeping a wide variety of items. In order to maintain user interest, it is necessary to suggest users the items that would help them to retain and increase their attraction towards products. This not only means showing items that would interest the users but also help the e-commerce companies to get profits out of sales. Thus, recommender systems come into picture. These systems are designed to help ecommerce companies help retain their user base. The recommender systems deploy a variety of different algorithms to study user preferences and make smart suggestions. Modern recommender engines are able to address only a single issue at a time. It is a trade-off between response time and accurate results that take into account variety of factors. This paper talks about the techniques that are used to build reliable and fast recommender systems as well as it discusses their working techniques.
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Gupta, P., Mathur, A.N., Kathuria, K., Chandak, R., Sangal, S. (2018). Recommender System Based on Fuzzy C-Means. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-10-8657-1_1
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DOI: https://doi.org/10.1007/978-981-10-8657-1_1
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