arXiv ·
Researchers studied user lifetime prediction in the location-based social network Jodel within Saudi Arabia, leveraging its disjoint communities. Machine learning models, particularly Random Forest, were trained to predict user lifetime as a regression and classification problem. A single countrywide model generalizes well and performs similarly to community-specific models.
arXiv ·
This paper introduces a mutually-regularized dual collaborative variational auto-encoder (MD-CVAE) for recommendation systems, addressing the limitations of user-oriented auto-encoders (UAEs) in handling sparse ratings and new items. MD-CVAE integrates item content and user ratings within a variational framework, regularizing UAE weights with item content to avoid non-optimal convergence. A symmetric inference strategy eliminates the need for retraining when introducing new items, enhancing efficiency in dynamic recommendation scenarios. Why it matters: The MD-CVAE approach offers a practical solution for improving recommendation accuracy and efficiency, especially in scenarios with data sparsity and frequent item updates, relevant to e-commerce and content platforms in the Middle East.