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.
|Title of host publication||The Web Conference 2021|
|Subtitle of host publication||Proceedings of the World Wide Web Conference, WWW 2021|
|Place of Publication||New York, NY|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||11|
|Publication status||Published - 2021|
|Event||2021 World Wide Web Conference, WWW 2021 - Ljubljana, Slovenia|
Duration: 19 Apr 2021 → 23 Apr 2021
|Name||The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021|
|Conference||2021 World Wide Web Conference, WWW 2021|
|Period||19/04/21 → 23/04/21|
Bibliographical noteCopyright 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.
- User cold-start recommendation
- Meta learning
- Neural process