An adversarial approach to improve long-tail performance in neural collaborative filtering

Adit Krishnan, Ashish Sharma, Aravind Sankar, Hari Sundaram

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

Abstract

In recent times, deep neural networks have found success in Collaborative Filtering (CF) based recommendation tasks. By parametrizing latent factor interactions of users and items with neural architectures, they achieve significant gains in scalability and performance over matrix factorization. However, the long-tail phenomenon in recommender performance persists on the massive inventories of online media or retail platforms. Given the diversity of neural architectures and applications, there is a need to develop a generalizable and principled strategy to enhance long-tail item coverage. In this paper, we propose a novel adversarial training strategy to enhance long-tail recommendations for users with Neural CF (NCF) models. The adversary network learns the implicit association structure of entities in the feedback data while the NCF model is simultaneously trained to reproduce these associations and avoid the adversarial penalty, resulting in enhanced long-tail performance. Experimental results show that even without auxiliary data, adversarial training can boost long-tail recall of state-of-the-art NCF models by up to 25%, without trading-off overall performance. We evaluate our approach on two diverse platforms, content tag recommendation in Q&A forums and movie recommendation.

Original languageEnglish (US)
Title of host publicationCIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
EditorsNorman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
PublisherAssociation for Computing Machinery
Pages1491-1494
Number of pages4
ISBN (Electronic)9781450360142
DOIs
StatePublished - Oct 17 2018
Event27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy
Duration: Oct 22 2018Oct 26 2018

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other27th ACM International Conference on Information and Knowledge Management, CIKM 2018
CountryItaly
CityTorino
Period10/22/1810/26/18

Keywords

  • Adversarial Learning
  • Long-Tail Phenomenon
  • Neural Collaborative Filtering
  • Recommender Systems

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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