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.

中文翻译：

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推荐与否：与预训练语言模型对话中的推荐性识别

当前大多数推荐系统主要关注推荐内容，假设用户总是需要个性化推荐。然而，随着ChatGPT和其他聊天机器人的广泛传播，会话系统中一个更关键的问题是，当我们为用户提供推荐服务时，如何最大限度地减少用户干扰。虽然之前的研究广泛探索了对话系统中的不同用户意图，但很少有人研究是否应该提供推荐。在本文中，我们正式定义了推荐性识别问题，旨在确定推荐在特定场景下是否必要。首先，我们提出并定义了可推荐性识别任务，该任务调查当前会话上下文中推荐的需求。构建了一个新的数据集。随后，我们讨论并评估利用预训练语言模型（PLM）进行推荐性识别的可行性。最后，通过对比实验，我们证明直接采用零样本结果的 PLM 无法满足任务要求。此外，微调或利用软提示技术可以产生与传统分类方法相当的结果。我们的工作是第一个在推荐之前研究可推荐性的工作，并提供了使其成为未来推荐系统的基本组成部分的初步方法。