Interests Burn-down Diffusion Process for Personalized Collaborative Filtering

TL;DR

This paper introduces a novel interests burn-down diffusion process tailored for collaborative filtering, improving personalized recommendations by better modeling user interest decay and recovery.

Contribution

It proposes a new diffusion scheme called interests burn-down process and demonstrates its effectiveness through the StageCF recommendation method.

Findings

StageCF outperforms existing generative and diffusion-based baselines.

The interests burn-down process effectively models user interest dynamics.

Experimental results validate the proposed method's superiority.

Abstract

Generative methods have gained widespread attention in Collaborative Filtering (CF) tasks for their ability to produce high-quality personalized samples aligned with users' interests. Among them, diffusion generative models have raised increasing attention in recommendation field. Despite that the pioneering efforts have applied the conventional diffusion process to model diffusive user interests, the incongruity between the Gaussian noise and the subtle nature of user's personalized interaction behavior has led to sub-optimal results. To this end, we introduce a specifically-tailored diffusion scheme for interaction systems, namely the interests burn-down process. The interests burn-down process delineates the decay of user interests towards candidate items, complemented by its reverse burn-up process that yields personalized recommendation for users. The inherent burn-down nature of this process adeptly models the diffusive user interests, aligning seamlessly with the requirements of CF tasks. We present a novel recommendation method StageCF to illustrate the superiority of this newly proposed diffusion process. Experimental results have demonstrated the effectiveness of StageCF against existing generative and diffusion-based baseline methods. Furthermore, comprehensive studies validate the functionality of interests burn-down process, shedding light on its capacity to generate personalized interactions.