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Poster
Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions
Yichen Wang · Nan Du · Rakshit Trivedi · Le Song

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #152 #None
in Wednesday Posters »

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.

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

Author Information

Yichen Wang (Georgia Tech)
Nan Du (Georgia Tech)
Rakshit Trivedi (Georgia Institute of Technolo)
Le Song (Georgia Institute of Technology)

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