Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75040
Title: Alternating pointwise-pairwise learning for personalized item ranking
Authors: Lei, Y 
Li, W 
Lu, Z 
Zhao, M 
Keywords: Collaborative ranking
Item recommendation
Personalized item ranking
Issue Date: 2017
Publisher: Association for Computing Machinery
Source: International Conference on Information and Knowledge Management, Proceedings, 2017, v. Part F131841, p. 2155-2158 How to cite?
Abstract: Pointwise and pairwise collaborative ranking are two major classes of algorithms for personalized item ranking. This paper proposes a novel joint learning method named alternating pointwise-pairwise learning (APPL) to improve ranking performance. APPL combines the ideas of both pointwise and pairwise learning, and is able to produce a more effective prediction model. The extensive experiments with both explicit and implicit feedback settings on four real-world datasets demonstrate that APPL performs significantly better than the state-of-the-art methods.
Description: 26th ACM International Conference on Information and Knowledge Management, CIKM 2017, Pan Pacific, Singapore, 6-10 November 2017
URI: http://hdl.handle.net/10397/75040
ISBN: 9781450349185
DOI: 10.1145/3132847.3133100
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Last Week
0
Last month
Citations as of Dec 8, 2018

Page view(s)

49
Citations as of Dec 10, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.