Most current recommender systems primarily focus on what to recommend, assuming users always require personalized recommendations. However, with the widely spread of ChatGPT and other chatbots, a more crucial problem in the context of conversational systems is how to minimize user disruption when we provide recommendation services for users. While previous research has extensively explored different user intents in dialogue systems, fewer efforts are made to investigate whether recommendations should be provided. In this paper, we formally define the recommendability identification problem, which aims to determine whether recommendations are necessary in a specific scenario. First, we propose and define the recommendability identification task, which investigates the need for recommendations in the current conversational context. A new dataset is constructed. Subsequently, we discuss and evaluate the feasibility of leveraging pre-trained language models (PLMs) for recommendability identification. Finally, through comparative experiments, we demonstrate that directly employing PLMs with zero-shot results falls short of meeting the task requirements. Besides, fine-tuning or utilizing soft prompt techniques yields comparable results to traditional classification methods. Our work is the first to study recommendability before recommendation and provides preliminary ways to make it a fundamental component of the future recommendation system.

翻译：当前大多数推荐系统主要关注推荐什么，假设用户始终需要个性化推荐。然而，随着ChatGPT等聊天机器人的广泛普及，在对话系统中一个更关键的问题是如何在为用户提供推荐服务时最小化对用户的干扰。尽管先前研究已广泛探讨对话系统中不同的用户意图，但关于是否应提供推荐的研究仍较少。本文首次正式定义了可推荐性识别问题，旨在判断特定场景下是否需要推荐。首先，我们提出并定义了可推荐性识别任务，该任务探究当前对话语境中推荐的必要性，并构建了一个新的数据集。随后，我们讨论并评估了利用预训练语言模型（PLMs）进行可推荐性识别的可行性。最后，通过对比实验，我们发现直接采用零样本学习的PLMs未能满足任务需求，而微调或使用软提示技术可取得与传统分类方法相当的结果。我们的工作是首个在推荐前研究可推荐性的探索，并为使其成为未来推荐系统的核心组成部分提供了初步路径。