Skip to yearly menu bar Skip to main content


Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions

Yichen Wang · Nan Du · Rakshit Trivedi · Le Song

Area 5+6+7+8 #152

Keywords: [ (Application) Collaborative Filtering and Recommender Systems ] [ Matrix Factorization ]


Matching users to the right items at the right time is a fundamental task in recommendation systems. As users interact with different items over time, users' and items' feature may evolve and co-evolve over time. Traditional models based on static latent features or discretizing time into epochs can become ineffective for capturing the fine-grained temporal dynamics in the user-item interactions. We propose a coevolutionary latent feature process model that accurately captures the coevolving nature of users' and items' feature. To learn parameters, we design an efficient convex optimization algorithm with a novel low rank space sharing constraints. Extensive experiments on diverse real-world datasets demonstrate significant improvements in user behavior prediction compared to state-of-the-arts.

Live content is unavailable. Log in and register to view live content