A Gated Hybrid Contrastive Collaborative Filtering Recommendation
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911633511546880 |
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| author | da Silva, Eduardo Ferreira Oliveira, Mayki dos Santos Pires, Joel Machado Boaventura, Denis Dantas Peixoto, Maycon Maciel Prazeres, Cassio Serafim Figueiredo, Gustavo Bittencourt Capretz, Miriam Durão, Frederico Araujo |
| author_facet | da Silva, Eduardo Ferreira Oliveira, Mayki dos Santos Pires, Joel Machado Boaventura, Denis Dantas Peixoto, Maycon Maciel Prazeres, Cassio Serafim Figueiredo, Gustavo Bittencourt Capretz, Miriam Durão, Frederico Araujo |
| contents | 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 refine the latent space, we introduce a contrastive learning module that aligns semantic and collaborative signals. We evaluate the framework across five distinct configurations: Pure collaborative; Topic and Gated; Text and Gated; and the addition of contrastive objectives (Contrastive and Topic, and Contrastive and Text). To explicitly optimize ranking behavior, the model is trained with a pairwise Bayesian personalized ranking objective, which promotes separation between relevant and non-relevant items in the latent space. Experiments on Amazon Movies & TV, IMDb, and Rotten Tomatoes demonstrate consistent improvements in hit rate @10 and normalized discounted cumulative gain @10 over state-of-the-art review-aware baselines. Results highlight the importance of controlled semantic fusion for ranking-driven recommendation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27117 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Gated Hybrid Contrastive Collaborative Filtering Recommendation da Silva, Eduardo Ferreira Oliveira, Mayki dos Santos Pires, Joel Machado Boaventura, Denis Dantas Peixoto, Maycon Maciel Prazeres, Cassio Serafim Figueiredo, Gustavo Bittencourt Capretz, Miriam Durão, Frederico Araujo Information Retrieval Artificial Intelligence 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 refine the latent space, we introduce a contrastive learning module that aligns semantic and collaborative signals. We evaluate the framework across five distinct configurations: Pure collaborative; Topic and Gated; Text and Gated; and the addition of contrastive objectives (Contrastive and Topic, and Contrastive and Text). To explicitly optimize ranking behavior, the model is trained with a pairwise Bayesian personalized ranking objective, which promotes separation between relevant and non-relevant items in the latent space. Experiments on Amazon Movies & TV, IMDb, and Rotten Tomatoes demonstrate consistent improvements in hit rate @10 and normalized discounted cumulative gain @10 over state-of-the-art review-aware baselines. Results highlight the importance of controlled semantic fusion for ranking-driven recommendation. |
| title | A Gated Hybrid Contrastive Collaborative Filtering Recommendation |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2604.27117 |