# Coupled Variational Recurrent Collaborative Filtering

## TL;DR

This paper introduces CVRCF, a novel deep Bayesian framework for streaming recommender systems that models dynamic user preferences and item popularities, improving accuracy and interpretability.

## Contribution

It integrates probabilistic models with deep neural networks using variational inference for the first time in streaming recommendations.

## Key findings

- Outperforms state-of-the-art methods on benchmark datasets
- Effectively models temporal dynamics of preferences and popularities
- Provides interpretable visualizations of evolving user-item interactions

## Abstract

We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner. Although deep neural networks have demonstrated the effectiveness of recommendation tasks, it is lack of explorations on integrating probabilistic models and deep architectures under streaming recommendation settings. Conjoining the complementary advantages of probabilistic models and deep neural networks could enhance both model effectiveness and the understanding of inference uncertainties. To bridge the gap, in this paper, we propose a Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to handle the streaming recommendation problem. The framework jointly combines stochastic processes and deep factorization models under a Bayesian paradigm to model the generation and evolution of users' preferences and items' popularities. To ensure efficient optimization and streaming update, we further propose a sequential variational inference algorithm based on a cross variational recurrent neural network structure. Experimental results on three benchmark datasets demonstrate that the proposed framework performs favorably against the state-of-the-art methods in terms of both temporal dependency modeling and predictive accuracy. The learned latent variables also provide visualized interpretations for the evolution of temporal dynamics.