Rethinking Convolutional Networks for Attribute-Aware Sequential Recommendation
Attribute-aware sequential recommendation entails predicting the next item a user will interact with based on a chronologically ordered history of past interactions, enriched with item attributes. Existing methods typically leverage self-attention mechanisms to aggregate the entire sequence into a unified representation used for next-item prediction. While effective, these models often suffer from high computational complexity and memory consumption, limiting their ability to process long user histories. This constraint restricts the model's capacity to fully capture long-term user preferences. In some scenarios, modeling item interactions purely through attention may also not be the most...
| Authors | Shereen Elsayed, Ngoc Son Le, Ahmed Rashed et al. |
|---|---|
| Primary category | cs.IR |
| Published | 6 May 2026 |
| Last updated at source | 7 May 2026 |
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