Task-adaptive neural process for user cold-start recommendation

Xixun Lin, Jia Wu, Chuan Zhou*, Shirui Pan, Yanan Cao, Bin Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)

Abstract

User cold-start recommendation is a long-standing challenge for recommender systems due to the fact that only a few interactions of cold-start users can be exploited. Recent studies seek to address this challenge from the perspective of meta learning, and most of them follow a manner of parameter initialization, where the model parameters can be learned by a few steps of gradient updates. While these gradient-based meta-learning models achieve promising performances to some extent, a fundamental problem of them is how to adapt the global knowledge learned from previous tasks for the recommendations of cold-start users more effectively.

In this paper, we develop a novel meta-learning recommender called task-adaptive neural process (TaNP). TaNP is a new member of the neural process family, where making recommendations for each user is associated with a corresponding stochastic process. TaNP directly maps the observed interactions of each user to a predictive distribution, sidestepping some training issues in gradient-based meta-learning models. More importantly, to balance the trade-off between model capacity and adaptation reliability, we introduce a novel task-adaptive mechanism. It enables our model to learn the relevance of different tasks and customize the global knowledge to the task-related decoder parameters for estimating user preferences. We validate TaNP on multiple benchmark datasets in different experimental settings. Empirical results demonstrate that TaNP yields consistent improvements over several state-of-the-art meta-learning recommenders.

Original languageEnglish
Title of host publicationThe Web Conference 2021
Subtitle of host publicationProceedings of the World Wide Web Conference, WWW 2021
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery, Inc
Pages1306-1316
Number of pages11
ISBN (Electronic)9781450383127
DOIs
Publication statusPublished - 2021
Event2021 World Wide Web Conference, WWW 2021 - Ljubljana, Slovenia
Duration: 19 Apr 202123 Apr 2021

Publication series

NameThe Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021

Conference

Conference2021 World Wide Web Conference, WWW 2021
CountrySlovenia
CityLjubljana
Period19/04/2123/04/21

Bibliographical note

Copyright the Publisher 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • User cold-start recommendation
  • Meta learning
  • Neural process

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