Re-ranking models refine item recommendation lists generated by the prior global ranking model, which have demonstrated their effectiveness in improving the recommendation quality. However, most existing re-ranking solutions only learn from implicit feedback with a shared prediction model, which regrettably ignore inter-item relationships under diverse user intentions. In this paper, we propose a novel Intention-aware Re-ranking Model with Dynamic Transformer Encoder (RAISE), aiming to perform user-specific prediction for each individual user based on her intentions. Specifically, we first propose to mine latent user intentions from text reviews with an intention discovering module (IDM). By differentiating the importance of review information with a co-attention network, the latent user intention can be explicitly modeled for each user-item pair. We then introduce a dynamic transformer encoder (DTE) to capture user-specific inter-item relationships among item candidates by seamlessly accommodating the learned latent user intentions via IDM. As such, one can not only achieve more personalized recommendations but also obtain corresponding explanations by constructing RAISE upon existing recommendation engines. Empirical study on four public datasets shows the superiority of our proposed RAISE, with up to 13.95%, 9.60%, and 13.03% relative improvements evaluated by Precision@5, MAP@5, and NDCG@5 respectively.

翻译：重排序模型通过优化先前全局排序模型生成的物品推荐列表，已被证明能有效提升推荐质量。然而，现有重排序方案大多仅从隐式反馈中学习，并采用共享预测模型，这遗憾地忽视了不同用户意图下的物品间关系。本文提出一种基于意图感知的动态Transformer编码器重排序模型（RAISE），旨在根据用户意图为每个用户执行个性化预测。具体而言：首先，我们提出通过意图发现模块（IDM）从文本评论中挖掘潜在用户意图。通过协同注意力网络区分评论信息的重要性，可为每个用户-物品对显式建模潜在用户意图。随后，我们引入动态Transformer编码器（DTE），通过无缝整合IDM学习到的潜在用户意图，捕捉物品候选项之间的用户特定关系。这种方法不仅能实现更个性化的推荐，还能通过将RAISE构建于现有推荐引擎之上获得相应解释。在四个公开数据集上的实证研究表明，所提出的RAISE模型具有优越性，在Precision@5、MAP@5和NDCG@5指标上分别达到13.95%、9.60%和13.03%的相对提升。