{
  "id": 5207,
  "url": "https://arxiv.org/abs/2604.27117v1",
  "title": "A Gated Hybrid Contrastive Collaborative Filtering Recommendation",
  "summary": "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",
  "authors": "Eduardo Ferreira da Silva, Mayki dos Santos Oliveira, Joel Machado Pires, Denis Dantas Boaventura, Maycon Maciel Peixoto, Cassio Serafim Prazeres et al.",
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-29T19:10:43.000Z",
  "fetched_at": "2026-07-14T16:31:35.574Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5207",
  "original_url": "https://arxiv.org/abs/2604.27117v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}