Traditional recommendation systems estimate user preference on items from
past interaction history, thus suffering from the limitations of obtaining
fine-grained and dynamic user preference. Conversational recommendation system
(CRS) brings revolutions to those limitations by enabling the system to
directly ask users about their preferred attributes on items. However, existing
CRS methods do not make full use of such advantage -- they only use the
attribute feedback in rather implicit ways such as updating the latent user
representation. In this paper, we propose Conversational Path Reasoning (CPR),
a generic framework that models conversational recommendation as an interactive
path reasoning problem on a graph. It walks through the attribute vertices by
following user feedback, utilizing the user preferred attributes in an explicit
way. By leveraging on the graph structure, CPR is able to prune off many
irrelevant candidate attributes, leading to better chance of hitting user
preferred attributes. To demonstrate how CPR works, we propose a simple yet
effective instantiation named SCPR (Simple CPR). We perform empirical studies
on the multi-round conversational recommendation scenario, the most realistic
CRS setting so far that considers multiple rounds of asking attributes and
recommending items. Through extensive experiments on two datasets Yelp and
LastFM, we validate the effectiveness of our SCPR, which significantly
outperforms the state-of-the-art CRS methods EAR (arXiv:2002.09102) and CRM
(arXiv:1806.03277). In particular, we find that the more attributes there are,
the more advantages our method can achieve.

本文提出了一种名为 CPR 的通用框架，将会话型推荐建模为图上的交互式路径推理问题，通过遵循用户反馈，以明确的方式利用用户首选属性，通过利用图结构，CPR 能够剪枝许多不相关的候选属性，从而更好地命中用户首选属性，实验证明了 CPR 的有效性，特别是我们发现，属性越多，我们的方法取得的优势就越大。