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http://dx.doi.org/10.5909/JBE.2019.24.3.515

Influential Factor Based Hybrid Recommendation System with Deep Neural Network-Based Data Supplement  

An, Hyeon-woo (Department of Computer Engineering, Hoseo University)
Moon, Nammee (Department of Computer Engineering, Hoseo University)
Publication Information
Journal of Broadcast Engineering / v.24, no.3, 2019 , pp. 515-526 More about this Journal
Abstract
In the real world, the user's preference for a particular product is determined by many factors besides the quality of the product. The reflection of these external factors was very difficult because of various fundamental problems including lack of data. However, access to external factors has become easier as the infrastructure for public data is opened and the availability of evaluation platforms with diverse and vast amounts of data. In accordance with these changes, this paper proposes a recommendation system structure that can reflect the collectable factors that affect user's preference, and we try to observe the influence of actual influencing factors on preference by applying case. The structure of the proposed system can be divided into a process of selecting and extracting influencing factors, a process of supplementing insufficient data using sentence analysis, and finally a process of combining and merging user's evaluation data and influencing factors. We also propose a validation process that can determine the appropriateness of the setting of the structural variables such as the selection of the influence factors through comparison between the result group of the proposed system and the actual user preference group.
Keywords
Hybrid Recommendation; influencing Factor; Recommendation System;
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