A novel recommendation model with Google similarity

NCU Institutional Repository > 管理學院 > 資訊管理學系 > 期刊論文 > Item 987654321/106285

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://ir.lib.ncu.edu.tw/handle/987654321/106285

题名:	A novel recommendation model with Google similarity
作者:	陳彥良;Huang, Tony Cheng-Kui;Chen, Yen-Liang;Chen, Min-Chun
贡献者:	管理學院資訊管理學系
关键词:	Collaboration;Collaborative filtering;Data base management;Data mining;Information science;Mathematical problems;Normalized Google distance;Recommender system;Recommender systems;Search engines;Sparsity;Studies
日期:	2016-09-01
上传时间:	2026-04-23 13:16:27 (UTC+8)
出版者:	Elsevier;Amsterdam: Elsevier B.V
摘要:	摘要： Previous studies on collaborative filtering have mainly adopted local resources as the basis for conducting analyses, and user rating matrices have been used to perform similarity analysis and prediction. Therefore, the efficiency and correctness of item-based collaborative filtering completely depend on the quantity and comprehensiveness of data collected in a rating matrix. However, data insufficiency leads to the sparsity problem. Additionally, cold-start is an inevitable problem concerning with how local resources are used as the basis for conducting analyses. This paper proposes a new idea by identifying an additional database to support item-based collaborative filtering. Regardless of whether a recommender system operates under a normal condition or applies a sparse matrix and introduces new items, this extra database can be used to accurately calculate item similarity. Moreover, prediction results acquired from two distinctive sets of data can be integrated to enhance the accuracy of the final prediction or successful recommendation. •Previous studies on collaborative filtering have adopted local resources as the basis.•The efficiency of item-based collaborative filtering depends on the quantity of data.•This paper proposes a new idea by identifying an additional database to support item-based collaborative filtering. 出版者： Amsterdam: Elsevier B.V 出版日期： 2016-09 出處： Decision Support Systems, 2016-09, Vol.89, p.17-27 資源來源： Elsevier ScienceDirect Journals Complete 版權： 2016 Elsevier B.V. 版權： Copyright Elsevier Sequoia S.A. Sep 2016 識別號： ISSN: 0167-9236 識別號： EISSN: 1873-5797 識別號： DOI: 10.1016/j.dss.2016.06.005 識別號： CODEN: DSSYDK
显示于类别:	[資訊管理學系] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	36	检视/开启

在NCUIR中所有的数据项都受到原著作权保护.

社群 sharing

数据加载中.....