Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach
This addresses the challenge of open-world recommendation where new users cannot be handled by traditional transductive methods, though it appears incremental as it builds on existing matrix factorization and attention mechanisms.
The paper tackles the problem of handling new users in recommendation systems by proposing an inductive collaborative filtering framework that learns user embeddings on-the-fly, achieving promising results for few-shot and unseen users in open-world scenarios.
Recommendation models can effectively estimate underlying user interests and predict one's future behaviors by factorizing an observed user-item rating matrix into products of two sets of latent factors. However, the user-specific embedding factors can only be learned in a transductive way, making it difficult to handle new users on-the-fly. In this paper, we propose an inductive collaborative filtering framework that contains two representation models. The first model follows conventional matrix factorization which factorizes a group of key users' rating matrix to obtain meta latents. The second model resorts to attention-based structure learning that estimates hidden relations from query to key users and learns to leverage meta latents to inductively compute embeddings for query users via neural message passing. Our model enables inductive representation learning for users and meanwhile guarantees equivalent representation capacity as matrix factorization. Experiments demonstrate that our model achieves promising results for recommendation on few-shot users with limited training ratings and new unseen users which are commonly encountered in open-world recommender systems.