Collaborative ranking with a push at the top

Konstantina Christakopoulou, Arindam Banerjee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Scopus citations

Abstract

The goal of collaborative filtering is to get accurate recommendations at the top of the list for a set of users. From such a perspective, collaborative ranking based formulations with suitable ranking loss functions are natural. While recent literature has explored the idea based on objective functions such as NDCG or Average Precision, such objectives are difficult to optimize directly. In this paper, building on recent advances from the learning to rank literature, we introduce a novel family of collaborative ranking algorithms which focus on accuracy at the top of the list for each user while learning the ranking functions collaboratively. We consider three specific formulations, based on collaborative p-norm push, infinite push, and reverse-height push, and propose efficient optimization methods for learning these models. Experimental results illustrate the value of collaborative ranking, and show that the proposed methods are competitive, usually better than existing popular approaches to personalized recommendation.

Original languageEnglish (US)
Title of host publicationWWW 2015 - Proceedings of the 24th International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Pages205-215
Number of pages11
ISBN (Electronic)9781450334693
DOIs
StatePublished - May 18 2015
Event24th International Conference on World Wide Web, WWW 2015 - Florence, Italy
Duration: May 18 2015May 22 2015

Publication series

NameWWW 2015 - Proceedings of the 24th International Conference on World Wide Web

Other

Other24th International Conference on World Wide Web, WWW 2015
Country/TerritoryItaly
CityFlorence
Period5/18/155/22/15

Bibliographical note

Funding Information:
The work was supported in part by NSF grants IIS-1447566, IIS-1422557,CCF-1451986, CNS-1314560, IIS-0953274, IIS-1029711, NASA grant NNX12AQ39A

Keywords

  • Collaborative ranking
  • Infinite push
  • Recommender systems

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