A Gated Hybrid Contrastive Collaborative Filtering Recommendation
Recommender systems increasingly incorporate textual reviews to enrich user and item representations. However, most review-aware models remain optimized for rating prediction rather than ranking quality. This misalignment limits their effectiveness in top-N recommendation scenarios, where discriminative ranking is essential. To address this gap, we propose a Gated Hybrid Collaborative Filtering framework that integrates review-derived representations into an autoencoder-based collaborative model. The architecture injects semantic signals layer-wise through an adaptive gating mechanism that dynamically balances collaborative embeddings and topic-based features during encoding. To further r...
| Authors | Eduardo Ferreira da Silva, Mayki dos Santos Oliveira, Joel Machado Pires et al. |
|---|---|
| Primary category | cs.IR |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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