Salespeople vs SalesBot: Exploring the Role of Educational Value in
  Conversational Recommender Systems
Lidiya Murakhovs'ka Philippe Laban Tian Xie Caiming Xiong Chien-Sheng Wu
Making big purchases requires consumers to research or consult a salesperson
to gain domain expertise. However, existing conversational recommender systems
(CRS) often overlook users' lack of background knowledge, focusing solely on
gathering preferences. In this work, we define a new problem space for
conversational agents that aim to provide both product recommendations and
educational value through mixed-type mixed-initiative dialog. We introduce
SalesOps, a framework that facilitates the simulation and evaluation of such
systems by leveraging recent advancements in large language models (LLMs). We
build SalesBot and ShopperBot, a pair of LLM-powered agents that can simulate
either side of the framework. A comprehensive human study compares SalesBot
against professional salespeople, revealing that although SalesBot approaches
professional performance in terms of fluency and informativeness, it lags
behind in recommendation quality. We emphasize the distinct limitations both
face in providing truthful information, highlighting the challenges of ensuring
faithfulness in the CRS context. We release our code and make all data
available.