Abstract
Recommendation system is the tool to user preferences over a given set of items. It takes help of the previous auxiliary information in terms of feedback or ratings. The main purpose of a recommender system is to engage users and enhance their experience over the Internet. Presently, recommender systems are widely used over e-commerce and social networking sites. The different applications require specialised recommendation system for them as e-commerce sites recommendation systems are different from social networking sites. So, recommendation system’s biggest challenge is the diversity as one cannot generate an accurate prediction using the same technique for different applications. This paper is an effort to illustrate one of the popular recommendation techniques, collaborative filtering based on classes, memory based and model based on two popular data sets (Movie lens and Jester). Further, it represents a comparative analysis of how results diverge from application to application and provides a way to optimise results of existing algorithm to get most out of them. The purpose is to present an exposure and open door to use more sophisticated data mining and machine learning techniques to enhance the overall efficiency of recommendation system.
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Raghuwanshi, S.K., Pateriya, R.K. (2019). Collaborative Filtering Techniques in Recommendation Systems. In: Shukla, R.K., Agrawal, J., Sharma, S., Singh Tomer, G. (eds) Data, Engineering and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-13-6347-4_2
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