Presentation 2019-07-22
A Recommendation System using Auto-encoders for Data Divided on Item Densities
Go Tanioka, Akihiro Inokuchi,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In recent years, deep learning have attracted attention in the field of machine learning, and its applications to the recommendation system dealt with in this study have also been proposed.In this paper, we improve the conventional recommendation system using an Auto-Encoder.In concrete terms, we propose a method for learning with two independent models for dense items and sparse items that constitute input data.We performed comparative experiments with the existing method and proposed method using MovieLens and other datasets to evaluate their superiority.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Recommendation System / AutoEncoder / Deep Learning
Paper # AI2019-14
Date of Issue 2019-07-15 (AI)

Conference Information
Committee AI
Conference Date 2019/7/22(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Naoki Fukuta(Shizuoka Univ.)
Vice Chair Yuichi Sei(Univ. of Electro-Comm.) / Yuko Sakurai(AIST)
Secretary Yuichi Sei(Osaka Univ.) / Yuko Sakurai(Tokyo Univ. of Agriculture and Technology)

Paper Information
Registration To Technical Committee on Artificial Intelligence and Knowledge-Based Processing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Recommendation System using Auto-encoders for Data Divided on Item Densities
Sub Title (in English)
Keyword(1) Recommendation System
Keyword(2) AutoEncoder
Keyword(3) Deep Learning
1st Author's Name Go Tanioka
1st Author's Affiliation Kwansei Gakuin University(KGU)
2nd Author's Name Akihiro Inokuchi
2nd Author's Affiliation Kwansei Gakuin University(KGU)
Date 2019-07-22
Paper # AI2019-14
Volume (vol) vol.119
Number (no) AI-139
Page pp.pp.71-75(AI),
#Pages 5
Date of Issue 2019-07-15 (AI)