Pairwise learning to recommend with both users’ and items’ contextual information-Reference-Cited by-同舟云学术

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Pairwise learning to recommend with both users’ and items’ contextual information

Published:2016-11 Issue:16 Volume:10 Page:2084-2090
ISSN:1751-8636
Container-title:IET Communications
language:en
Short-container-title:IET Communications

Author:

Huang Zhenhua¹,E Shijia¹,Zhang Jiawen¹,Zhang Bo²,Ji Zilian³

Affiliation:

1. College of Electronics and Information EngineeringTongji UniversityShanghai201804People's Republic of China

2. College of Information, Mechanical and Electrical EngineeringShanghai Normal UniversityShanghai200234People's Republic of China

3. IBM China Systems and Technology LaboratoryIBM Greater China GroupShanghai201203People's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Science Applications

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-com.2016.0112

Reference22 articles.

1. Amazon.com recommendations: item-to-item collaborative filtering

2. Learning to Rank for Information Retrieval

3. KaratzoglouA.BaltrunasL.ShiY.: ‘Learning to rank for recommender systems’. Proc. of the 7th ACM Conf. on Recommender Systems 2013 pp.493–494

4. LiX.CongG.LiX.L.et al.: ‘Rank‐GeoFM: a ranking based geographical factorization method for point of interest recommendation’. Proc. of the 38th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval 2015 pp.433–442

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