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A Survey on Building Recommendation Systems Using Data Mining Techniques

A Survey on Building Recommendation Systems Using Data Mining Techniques
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Author(s): Rajab Ssemwogerere (Islamic University in Uganda, Uganda), Wamwoyo Faruk (Makerere University, Uganda) and Nambobi Mutwalibi (Islamic University in Uganda, Uganda)
Copyright: 2022
Pages: 33
Source title: Data Mining Approaches for Big Data and Sentiment Analysis in Social Media
Source Author(s)/Editor(s): Brij B. Gupta (Asia University, Taiwan), Dragan Peraković (University of Zagreb, Croatia), Ahmed A. Abd El-Latif (Menoufia University, Egypt) and Deepak Gupta (LoginRadius Inc., Canada)
DOI: 10.4018/978-1-7998-8413-2.ch002

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Abstract

Classification is a data mining technique or approach used to estimate the grouped membership of items on a basis of a common feature. This technique is virtuous for future planning and discovering new knowledge about a specific dataset. An in-depth study of previous pieces of literature implementing data mining techniques in the design of recommender systems was performed. This chapter provides a broad study of the way of designing recommender systems using various data mining classification techniques of machine learning and also exploiting their methodological decisions in four aspects, the recommendation approaches, data mining techniques, recommendation types, and performance measures. This study focused on some selected classification methods and can be so supportive for both the researchers and the students in the field of computer science and machine learning in strengthening their knowledge about the machine learning hypothesis and data mining.

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