A Gated Hybrid Contrastive Collaborative Filtering Recommendation

TL;DR

This paper introduces a Gated Hybrid Contrastive Collaborative Filtering framework that enhances ranking quality in review-aware recommender systems by integrating semantic signals through gating and contrastive learning.

Contribution

It proposes a novel architecture combining adaptive gating and contrastive learning to improve top-N recommendation performance using review-derived representations.

Findings

Consistent improvements in hit rate @10 over baselines.

Enhanced NDCG @10 with the proposed model.

Effective semantic fusion for ranking in review-aware systems.

Abstract

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.