Towards Flexible and Adaptive Neural Process for Cold-Start Recommendation

Abstract

Recommender systems have been widely adopted in various online personal e-commerce applications for improving user experience. A long-standing challenge in recommender systems is how to provide accurate recommendation to users in cold-start situations where only a few user-item interactions can be observed. Recently, meta learning methods provide a promising solution, and most of them follow a way of parameter initialization where predictions can be fast adapted via multiple gradient descent steps. While these meta-learning recommenders promote model performance, how to derive a fundamental paradigm that enables both flexible approximations of complex user interaction distributions and effective task adaptations of global knowledge still remains a critical yet under-explored problem. To this end, we present the F low-based A daptive N eural P rocess (FANP), a new probabilistic meta …

Shirui Pan

Professor and ARC Future Fellow

My research interests include data mining, machine learning, and graph analysis.