  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.
