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http://dx.doi.org/10.9728/dcs.2017.18.4.707

A Recommender System Using Factorization Machine  

Jeong, Seung-Yoon (Division of Information Security Graduate School of Information Security, Korea University)
Kim, Hyoung Joong (Division of Information Security Graduate School of Information Security, Korea University)
Publication Information
Journal of Digital Contents Society / v.18, no.4, 2017 , pp. 707-712 More about this Journal
Abstract
As the amount of data increases exponentially, the recommender system is attracting interest in various industries such as movies, books, and music, and is being studied. The recommendation system aims to propose an appropriate item to the user based on the user's past preference and click stream. Typical examples include Netflix's movie recommendation system and Amazon's book recommendation system. Previous studies can be categorized into three types: collaborative filtering, content-based recommendation, and hybrid recommendation. However, existing recommendation systems have disadvantages such as sparsity, cold start, and scalability problems. To improve these shortcomings and to develop a more accurate recommendation system, we have designed a recommendation system as a factorization machine using actual online product purchase data.
Keywords
Recommendation System; Collaborative Filtering; Matrix Factorization; Factorization Machine; SVD;
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Times Cited By KSCI : 1  (Citation Analysis)
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