User-generated reviews contain rich semantics that can reveal users’ fine-grained preferences beyond what interaction data alone can capture. However, existing review-based recommender systems often fail to achieve semantic alignment between review content and user–item interactions, as they either treat reviews as coarse textual signals or rely on sentiment-oriented heuristics. Achieving such fine-grained alignment is challenging due to the inherent complexity and context dependence of review texts, as well as the semantic gap between explicit review information and implicit interaction preferences. To bridge this gap, we propose LLM-ASAR, a Large Language Model-driven framework for Aspect-based Semantic Alignment in Review-based Recommendation that explicitly aligns aspect-level review semantics with user–item interaction patterns. Specifically, LLM-ASAR leverages LLMs with Chain-of-Thought (CoT) prompting to perform sentence-level reasoning on user reviews, extracting aspect-specific semantics that are structurally integrated into aspect-specific interaction graphs. A multi-aspect contrastive alignment mechanism is further introduced to align user and item embeddings with corresponding aspect-level review features, ensuring consistency across modalities. Extensive experiments on three real-world datasets demonstrate that LLM-ASAR not only achieves significant accuracy improvements over state-of-the-art baselines but also yields interpretable insights into user preferences through its aspect-aware design. Our data and code are available at https://github.com/HuilinChenJN/LLM-ASAR.
Huilin Chen, Zhi-Yong Cheng, Fan Liu et al.· ACM Transactions on Recommen...· 0 citations
Open-Vocabulary Multi-Label Image Classification (OV-MLIC) is an emerging task in computer vision aimed at recognizing unseen categories in real-world scenarios, leveraging Vision and Language Pre-training (VLP) models like CLIP. However, existing methods overlook the impact of category coupling and scale variation on cross-category knowledge transfer, thereby restricting performance on unseen categories. To address this issue, we propose a novel OV-MLIC method called Hierarchical Dual-Granularity Alignment-CLIP (HDG-CLIP), which emphasizes the complementary characteristics of different modalities and introduces a sample-category matching mechanism. Specifically, to address the category coupling issue, we construct semantic category prototypes to enhance cross-category knowledge transfer. Through the interaction between visual embeddings and category prototypes, we decouple category-specific information from mixed visual features and leverage the visual context of samples to learn category-level visual features. For mitigating the scale variation issue, we build a sample-category dual-granularity matching mechanism based on the difference in capture capability of different modalities across scales, thereby improving the object localization accuracy from a multi-dimensional perspective. Extensive experimental results show that HDG-CLIP exhibits state-of-art performance over existing methods on both the NUS-WIDE and the Open-Images datasets. Our code is available at https://github.com/wakihy/HDG-CLIP
Beiyan Liu, Sheng Huang, Bo Liu et al.· IEEE Transactions on Image P...· 0 citations
Molecular properties are often governed by a small number of local substructures, or motifs, whose topologies can vary drastically across molecules. Existing molecular representation learning approaches typically embed all motifs into a single Euclidean or fixed-curvature space, which fails to capture the motif-level topological heterogeneity and leads to geometric mismatch, impairing property prediction. To address this challenge, we propose a geometry-adaptive Riemannian framework for molecular representation learning, which explicitly models motifs as the basic units and learns their embeddings across multiple constant-curvature spaces. Each motif is adaptively aligned with the geometric space that best fits its intrinsic topology, enabling simultaneous modeling of cyclic, hierarchical, and tree-like structures. Motif embeddings are then aggregated into molecule-level representations, emphasizing functional substructures while suppressing irrelevant background. Extensive experiments on benchmark molecular property prediction datasets demonstrate that our approach outperforms state-of-the-art baselines, shows strong generalization under distribution shifts, and provides interpretable motif-level insights, offering a general and scalable framework for scientific molecular modeling. Our code is available at https://github.com/qimuya/mo-mi-r.
Fei Liu, Wen-Kai Lu, Feilong Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Uni-AdaVD is presented, a universal inference-time concept erasure framework for visual generation that treats the value space of multimodal attention as a unified intervention space and introduces encoder-aware target representation construction to localize target semantics across heterogeneous text encoders.
A rationale-guided knowledge distillation framework for cross-lingual stance detection using Chain-of-Thought prompting to guide Large Language Models in generating informative rationales, and distill the resulting reasoning knowledge into a compact student model.
