OMuleT: Orchestrating Multiple Tools for Practicable Conversational Recommendation

TL;DR

This paper introduces OMuleT, a conversational recommender system that equips large language models with over 10 tools to handle real user requests effectively, providing relevant and diverse recommendations through extensive evaluation and deployment insights.

Contribution

It presents a novel multi-tool approach for LLM-based conversational recommenders, addressing real user needs and demonstrating improved performance over vanilla models.

Findings

Enhanced recommendation relevance and diversity

Effective use of extensive toolset in LLMs

Successful deployment with practical insights

Abstract

In this paper, we present a systematic effort to design, evaluate, and implement a realistic conversational recommender system (CRS). The objective of our system is to allow users to input free-form text to request recommendations, and then receive a list of relevant and diverse items. While previous work on synthetic queries augments large language models (LLMs) with 1-3 tools, we argue that a more extensive toolbox is necessary to effectively handle real user requests. As such, we propose a novel approach that equips LLMs with over 10 tools, providing them access to the internal knowledge base and API calls used in production. We evaluate our model on a dataset of real users and show that it generates relevant, novel, and diverse recommendations compared to vanilla LLMs. Furthermore, we conduct ablation studies to demonstrate the effectiveness of using the full range of tools in our toolbox. We share our designs and lessons learned from deploying the system for internal alpha release. Our contribution is the addressing of all four key aspects of a practicable CRS: (1) real user requests, (2) augmenting LLMs with a wide variety of tools, (3) extensive evaluation, and (4) deployment insights.