Scalable Personalised Item Ranking through Parametric Density Estimation

Riku Togashi, Masahiro Kato, Mayu Otani, Tetsuya Sakai, Shin'ichi Satoh

研究成果: Conference contribution

抄録

Learning from implicit feedback is challenging because of the difficult nature of the one-class problem: we can observe only positive examples. Most conventional methods use a pairwise ranking approach and negative samplers to cope with the one-class problem. However, such methods have two main drawbacks particularly in large-scale applications; (1) the pairwise approach is severely inefficient due to the quadratic computational cost; and (2) even recent model-based samplers (e.g. IRGAN) cannot achieve practical efficiency due to the training of an extra model. In this paper, we propose a learning-to-rank approach, which achieves convergence speed comparable to the pointwise counterpart while performing similarly to the pairwise counterpart in terms of ranking effectiveness. Our approach estimates the probability densities of positive items for each user within a rich class of distributions, viz. exponential family. In our formulation, we derive a loss function and the appropriate negative sampling distribution based on maximum likelihood estimation. We also develop a practical technique for risk approximation and a regularisation scheme. We then discuss that our single-model approach is equivalent to an IRGAN variant under a certain condition. Through experiments on real-world datasets, our approach outperforms the pointwise and pairwise counterparts in terms of effectiveness and efficiency.

本文言語English
ホスト出版物のタイトルSIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
出版社Association for Computing Machinery, Inc
ページ921-931
ページ数11
ISBN(電子版)9781450380379
DOI
出版ステータスPublished - 2021 7 11
イベント44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021 - Virtual, Online, Canada
継続期間: 2021 7 112021 7 15

出版物シリーズ

名前SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021
国/地域Canada
CityVirtual, Online
Period21/7/1121/7/15

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 情報システム

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