Reformulating Conversational Recommender Systems as Tri-Phase Offline Policy Learning

TL;DR

This paper presents TCRS, a novel offline learning framework for conversational recommender systems that models dynamic user preferences, reducing reliance on real-time interactions and improving robustness and accuracy in diverse scenarios.

Contribution

It introduces a tri-phase offline policy learning approach that better captures evolving user preferences and enhances recommendation performance without real-time user interaction.

Findings

TCRS outperforms traditional CRS models in diverse scenarios.

The approach improves robustness and adaptability of recommendations.

Enhanced understanding of user behavior dynamics.

Abstract

Existing Conversational Recommender Systems (CRS) predominantly utilize user simulators for training and evaluating recommendation policies. These simulators often oversimplify the complexity of user interactions by focusing solely on static item attributes, neglecting the rich, evolving preferences that characterize real-world user behavior. This limitation frequently leads to models that perform well in simulated environments but falter in actual deployment. Addressing these challenges, this paper introduces the Tri-Phase Offline Policy Learning-based Conversational Recommender System (TCRS), which significantly reduces dependency on real-time interactions and mitigates overfitting issues prevalent in traditional approaches. TCRS integrates a model-based offline learning strategy with a controllable user simulation that dynamically aligns with both personalized and evolving user preferences. Through comprehensive experiments, TCRS demonstrates enhanced robustness, adaptability, and accuracy in recommendations, outperforming traditional CRS models in diverse user scenarios. This approach not only provides a more realistic evaluation environment but also facilitates a deeper understanding of user behavior dynamics, thereby refining the recommendation process.