| Item type |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2021-03-09 |
| 書誌情報 |
en : IEEE Transactions on Computational Social Systems
巻 7,
号 3,
p. 672-685,
発行日 2020
|
| タイトル |
|
|
タイトル |
Alleviating New User Cold-Start in User-Based Collaborative Filtering via Bipartite Network |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Bipartite network |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
new user cold-start |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
recommender systems (RSs) |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
user-based collaborative filtering (UBCF) |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
張, 志鵬
董, 冕雄
太田, 香
工藤, 康生
|
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
董 冕雄（DONG Mianxiong） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000145_ja.html |
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
太田 香（OTA Kaoru） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000140_ja.html |
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
工藤 康生（KUDO Yasuo） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000129_ja.html |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
The recommender system (RS) can help us extract valuable data from a huge amount of raw information. User-based collaborative filtering (UBCF) is widely employed in practical RSs due to its outstanding performance. However, the traditional UBCF is subject to the new user cold-start issue because a new user is often extreme lack of available rating information. In this article, we develop a novel approach that incorporates a bipartite network into UBCF for enhancing the recommendation quality of new users. First, through the statistic and analysis of new users' rating characteristics, we collect niche items and map the corresponding rating matrix to a weighted bipartite network. Furthermore, a new weighted bipartite modularity index merging normalized rating information is present to conduct the community partition that realizes coclustering of users and items. Finally, for each individual clustering that is much smaller than the original rating matrix, a localized low-rank matrix factorization is executed to predict rating scores for unrated items. Items with the highest predicted rating scores are recommended to a new user. Experimental results from two real-world data sets suggest that without requiring additional complex information, the proposed approach is superior in terms of both recommendation accuracy and diversity and can alleviate the new user cold-start issue of UBCF effectively. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
IEEE |
|
言語 |
en |
| 出版者版へのリンク |
|
|
表示名 |
10.1109/TCSS.2020.2971942 |
|
URL |
https://doi.org/10.1109/TCSS.2020.2971942 |
| DOI |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.1109/TCSS.2020.2971942 |
| 日本十進分類法 |
|
|
主題Scheme |
NDC |
|
主題 |
007 |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2329-924X |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| 著者版フラグ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| フォーマット |
|
|
内容記述タイプ |
Other |
|
内容記述 |
application/pdf |
TCSS_7_3_672_685 (3.6 MB)
