Neural collaborative filtering with multicriteria evaluation data

Morise, Hiroki; Atarashi, Kyohei; Oyama, Satoshi; Kurihara, Masahito

doi:10.1016/j.asoc.2022.108548


Hokkaido University \| Library \| HUSCAP	Advanced Search		言語

	Home
	About HUSCAP
	Open Access Policy

	Browse by Author

Browse
	Communities & Collections

	Scholarly Journals
	Theses
	Doctoral Dissertations Listed by Graduate Schools
	Conference Procs.
	Events

	HUSCAP Senior (in Japanese)

	Societies

	Downloads (country)

For university staff
	How to post your papers to HUSCAP
	Publication of theses
	Helpline about theses publication

Open Archives Compliant

You can search our collection also at:
	Google
	Google Scholar
	CiNii
	IRDB
	OAIster
	NDLTD

Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >

Neural collaborative filtering with multicriteria evaluation data

Files in This Item:

The file(s) associated with this item can be obtained from the following URL: https://doi.org/10.1016/j.asoc.2022.108548

Title:	Neural collaborative filtering with multicriteria evaluation data
Authors:	Morise, Hiroki Browse this author
	Atarashi, Kyohei Browse this author
	Oyama, Satoshi Browse this author →KAKEN DB
	Kurihara, Masahito Browse this author →KAKEN DB
Keywords:	Recommendation
	Multicriteria rating
	Collaborative filtering
	Rating aggregation
	Deep learning
Issue Date:	Apr-2022
Publisher:	Elsevier
Journal Title:	Applied Soft Computing
Volume:	119
Start Page:	108548
Publisher DOI:	10.1016/j.asoc.2022.108548
Abstract:	Recommendation systems help consumers find useful items of information given a large amount of information while avoiding information overload. Nowadays, in addition to traditional evaluation information (such as individual reviewer ratings), information such as multicriteria ratings are available on the Web. In the work reported here, we investigated whether collaborative filtering methods using multicriteria evaluation data and deep learning are effective for abundant and sparse multicriteria evaluation data. We also investigated whether adaptability can be achieved by predicting aggregated ratings from the evaluations of a few users. Experimental results show that three proposed methods using deep learning are better than traditional methods for both recommendation and rating aggregation.
Type:	article
URI:	http://hdl.handle.net/2115/87708
Appears in Collections:	情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小山聡

OAI-PMH ( junii2 , jpcoar_1.0 )

- Hokkaido University