A Classification Framework Towards Application of Data Mining in Collaborative Filtering

A Classification Framework Towards Application of Data Mining in Collaborative Filtering

Neeti Sangwan (Maharaja Surajmal Institute of Technology, India) and Naveen Dahiya (Maharaja Surajmal Institute of Technology, India)
Copyright: © 2017 |Pages: 15
DOI: 10.4018/978-1-5225-0489-4.ch005
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Abstract

Recommendation making is an important part of the information and e-commerce ecosystem. Recommendation represent a powerful method that filter large amount of information to provide relevant choice to end users. To provide recommendations to the users, efficient and cost effective methods needs to be introduced. Collaborative filtering is an emerging technique used in making recommendations which makes use of filtering by data mining. This chapter presents a classification framework on the use of data mining techniques in collaborative filtering to extract the best recommendations to the users on the basis of their interests.
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Research Methodology

In this chapter, various collaborative filtering techniques are studied for improving the quality of the recommendation process. The research starts with the study of recommender systems namely: Collaborative Filtering, Content Based filtering, Hybrid Recommender, Demographic based filtering and Utility Based Filtering. The features and properties of recommender system are overviewed. Then we identified collaborative filtering as the most promising approach towards recommender systems. Collaborative filtering is an approach to make the recommendations based on the user and other persons past behavior and predict the items on basis of their interest. Collaborative filtering is further classified as memory based filtering, model Based filtering and hybrid filtering. Various application areas of collaborative filtering include: Books, Social Networking Sites, Movies, Music, Images, and Shopping etc. (Figure 1).

Figure 1.

Research roadmap

Best recommender system used collaborative filtering based on Data Mining approach. Data mining is the process of discovering interesting knowledge from large amounts of data stored in databases, data warehouses, or other information repositories” (Han & Kamber, 2001). Finally, authors come up with a broad classification framework showing the use of data mining in collaborative filtering for effective recommendations to the users. Figure 1 shows the research roadmap that has been followed in the chapter.

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