Graph Contrastive Learning (GCL) methods for recommendation learn representations by propagating signals over user-item interaction graphs. However, modeling these graphs with homogeneous edges can lead to semantic-structural misalignment, where information is exchanged between structurally adjacent but semantically dissimilar items, adversely affecting retrieval quality. Existing solutions typically rely on auxiliary encoders or additional supervision, increasing model complexity and training cost. We propose ACE (Affective Contrastive Embeddings), a framework that improves representation alignment by incorporating affective semantics into contrastive learning. ACE encodes the affective dimensions of valence and arousal as a topological prior, encouraging consistency between learned embeddings and an affective semantic space distilled from large language models. To operationalize this alignment, we introduce a Semantically Weighted Noise Contrastive Estimation (SW-NCE) loss that modulates contrastive gradients according to users' affective preferences. Experiments on Amazon, Last.fm, and SiTunes demonstrate that ACE consistently improves top-K retrieval performance over 11 baselines while reducing computational overhead. These results indicate that affective geometric alignment is an effective and efficient mechanism for enhancing graph-based retrieval models.

Maintaining a consistent brand identity across a global network of retail stores while adhering to local constraints has long challenged Visual Merchandisers.Legacy processes, often reliant on subjective "by-eye" adjustments, can drive up operational costs and lead to inconsistent in-store execution.We formalize a user modeling framework implementing a multi-criteria utility function that balances brand identity and operational overhead.We integrated our framework in a 3D virtual tour design platform, deploying it in the ecosystem of OVS, a global fashion firm.Through a preliminary user study, we showcase that our solution enables lower iteration cycles and decreases store-to-store discrepancies.