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2020 IEEE International Conference on Big Data (Big Data) by Lourenco, Jessica (author);Varde, Aparna S. (author) is a nonfiction available to read on EtoBox.

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In the ever-growing data-driven world today, data increases in many forms, e.g. e-commerce sites uploading new products, streaming services adding TV shows and movies, and music platforms uploading new songs. It would be highly infeasible for end users to quickly browse all this data. Hence recommender systems can benefit end users (individuals as well as companies) in efficiently finding suitable products. Rather than making end users search through a vast array of items, recommender systems can suggest suitable items to users based on popularity of the items and the respective users' buying behavior. Accordingly, in this paper we explore two techniques widespread in recommender systems, i.e. item-based collaborative filtering and association rule mining, over Amazon review data on cellphones and accessories, and build a baseline recommender system scalable to larger data. Association rule mining is explored using the Apriori algorithm to find patterns in the data from transaction history. Item-based collaborative filtering is deployed using a correlation matrix to find similar products. Both these techniques yield useful results as evident from our baseline experiments. This work

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Author

Lourenco, Jessica (author);Varde, Aparna S. (author)

Publisher

IEEE

Published

2020

Language

EN

Category

nonfiction