MIDA: Multi-Interest Decoding with Adaptive Aggregation for Session-based Recommendation

Overview & Synopsis

Session-based recommendation (SBR) infers user interest from limited interaction sequences, yet current models show a consistent performance drop when longer histories are available. This degradation is associated with decoder designs that overemphasize recent items while underutilizing distant, yet semantically relevant, interactions, leading to poor modeling of long-range dependencies and multi-interest behaviors. To address this challenge, we introduce MIDA (Multi-Interest Decoding with Adaptive Aggregation for Session-based Recommendation), a lightweight and encoder-agnostic framework that provides a reformulation of the decoding stage. The central design principle is a two-space decoupling: clustering operates on raw item embeddings so that semantic groupings are not distorted by encoder-learned relational biases; encoder outputs are reserved for aggregation, where sequential context remains intact. Concretely, MIDA (i) employs differentiable multi-interest clustering on raw embeddings, preserving semantic structure that encoder representations would otherwise obscure, and (ii) fuses stability and plasticity branches through a gate conditioned on average cluster size, so that the inferred clustering structure directly governs aggregation behavior through a structural coupling across the two stages. Extensive experiments on multiple benchmark datasets demonstrate that MIDA consistently outperforms state-of-the-art methods, with meaningful accuracy improvements while remaining computationally practical.