In this paper, we analyze the performance of a multitask end-to-end
transformer model on the task of conversational recommendations, which aim to
provide recommendations based on a user's explicit preferences expressed in
dialogue. While previous works in this area adopt complex multi-component
approaches where the dialogue management and entity recommendation tasks are
handled by separate components, we show that a unified transformer model, based
on the T5 text-to-text transformer model, can perform competitively in both
recommending relevant items and generating conversation dialogue. We fine-tune
our model on the ReDIAL conversational movie recommendation dataset, and create
additional training tasks derived from MovieLens (such as the prediction of
movie attributes and related movies based on an input movie), in a multitask
learning setting. Using a series of probe studies, we demonstrate that the
learned knowledge in the additional tasks is transferred to the conversational
setting, where each task leads to a 9%-52% increase in its related probe score.

本文基于 T5 文本转文本的 transformer 模型，将多任务端到端模型应用于对话推荐任务，实现了推荐相关项目和生成对话的双重目标，其中多任务学习和来自 ReDIAL 数据集的探针研究得到了应用。

多任务端到端训练改善对话式推荐

Multi-Task End-to-End Training Improves Conversational Recommendation

A conversational recommender system (CRS) is a practical application for item
recommendation through natural language conversation. Such a system estimates
user interests for appropriate personalized recommendations. Users sometimes
have various interests in different categories or genres, but existing studies
assume a unique user interest that can be covered by closely related items. In
this work, we propose to model such multiple user interests in CRS. We
investigated its effects in experiments using the ReDial dataset and found that
the proposed method can recommend a wider variety of items than that of the
baseline CR-Walker.

本文提出了在对话推荐系统中对用户多个兴趣点的建模方法，实验证明此方法可以比基准模型 CR-Walker 推荐更广泛的物品。