Attention over Self-attention:Intention-aware Re-ranking with Dynamic Transformer Encoders for Recommendation

Abstract

Re-ranking models refine the item recommendation list generated by the prior global ranking model with intra-item relationships. However, most existing re-ranking solutions refine recommendation list based on the implicit feedback with a shared re-ranking model, which regrettably ignore the intra-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 target 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 intra-item relationships among item candidates by seamlessly accommodating the learnt latent user intentions via IDM. As such, RAISE is able to perform user-specific prediction without increasing the depth (number of blocks) and width (number of heads) of the prediction model. Empirical study on four public datasets shows the superiority of our proposed RAISE, with up to 13.95%, 12.30%, and 13.03% relative improvements evaluated by Precision, MAP, and NDCG respectively.

Neural Re-ranking in Multi-stage Recommender Systems: A Review

“…The final prediction is generated by capturing the interactions between candidate items and users' multiple behaviors. A more recent work Raiss [Lin et al, 2022] attempts to improve personalization in re-ranking by maintaining individual attention weights in modeling cross-item interactions for each user. Other network structures.…”

“…The network parameters are shared across users. While the latter maintains an individual set of parameters for each user as in Raiss [Lin et al, 2022] or IRGPR [Liu et al, 2020b]. Complexity.…”

“…M 2 DIV [Feng et al, 2018] While for explicit approaches, notice the advantage of using the attention mechanism to determine the importance for the under-covered subtopics, and propose DSSA, where the relevance and the diversity are jointly estimated with a subtopic attention. As a follow-up to DSSA, DVGAN [Liu et al, 2020a] formulates the problem of generating diverse re-ranking lists as a minimax game. It adapts DSSA as a generator, and involves a discriminator to determine how relevant and diverse the given list is.…”

Neural Re-ranking in Multi-stage Recommender Systems: A Review

“…The final prediction is generated by capturing the interactions between candidate items and users' multiple behaviors. Raiss [Lin et al, 2022] attempts to improve personalization in re-ranking by maintaining individual attention weights in modeling cross-item interactions for each user. A more recent work PEAR proposes to model cross-item interactions between both the initial list and the users' historical clicked items by a designed cross-attention structure.…”

“…The network parameters are shared across users. While the latter maintains an individual set of parameters for each user as in Raiss [Lin et al, 2022] or IRGPR [Liu et al, 2020b].…”