  Recommender systems trained on offline historical user behaviors are
embracing conversational techniques to online query user preference. Unlike
prior conversational recommendation approaches that systemically combine
conversational and recommender parts through a reinforcement learning
framework, we propose CORE, a new offline-training and online-checking paradigm
that bridges a COnversational agent and REcommender systems via a unified
uncertainty minimization framework. It can benefit any recommendation platform
in a plug-and-play style. Here, CORE treats a recommender system as an offline
relevance score estimator to produce an estimated relevance score for each
item; while a conversational agent is regarded as an online relevance score
checker to check these estimated scores in each session. We define uncertainty
as the summation of unchecked relevance scores. In this regard, the
conversational agent acts to minimize uncertainty via querying either
attributes or items. Based on the uncertainty minimization framework, we derive
the expected certainty gain of querying each attribute and item, and develop a
novel online decision tree algorithm to decide what to query at each turn.
Experimental results on 8 industrial datasets show that CORE could be
seamlessly employed on 9 popular recommendation approaches. We further
demonstrate that our conversational agent could communicate as a human if
empowered by a pre-trained large language model.
