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

Google ScholarTM

Check

Altmetric


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