Improving One-class Recommendation with Multi-tasking on Various Preference Intensities
Chu-Jen Shao, Hao-Ming Fu, Pu‐Jen Cheng · 2020
In the one-class recommendation problem, it’s required to make recommendations basing on users’ implicit feedback, which is inferred from their action and inaction. Existing works obtain representations of users and items by encoding positive and negative interactions observed from training data. However, these efforts assume that all positive signals from implicit feedback reflect a fixed preference intensity, which is not realistic. Consequently, representations learned with these methods usually fail to capture informative entity features that reflect various preference intensities.