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Fuwei Zhang

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3 papers

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AAAI Conference 2026 Conference Paper

Multi-Aspect Cross-modal Quantization for Generative Recommendation

Fuwei Zhang
Xiaoyu Liu
Dongbo Xi
Jishen Yin
Huan Chen
Peng Yan
Fuzhen Zhuang
Zhao Zhang

Generative Recommendation (GR) has emerged as a new paradigm in recommender systems. This approach relies on quantized representations to discretize item features, modeling users’ historical interactions as sequences of discrete tokens. Based on these tokenized sequences, GR predicts the next item by employing next-token prediction methods. The challenges of GR lie in constructing high-quality semantic identifiers (IDs) that are hierarchically organized, minimally conflicting, and conducive to effective generative model training. However, current approaches remain limited in their ability to harness multimodal information and to capture the deep and intricate interactions among diverse modalities, both of which are essential for learning high-quality semantic IDs and for effectively training GR models. To address this, we propose Multi-Aspect Cross-modal quantization for generative Recommendation (MACRec), which introduces multimodal information and incorporates it into both semantic ID learning and generative model training from different aspects. Specifically, we first introduce cross-modal quantization during the ID learning process, which effectively reduces conflict rates and thus improves codebook usability through the complementary integration of multimodal information. In addition, to further enhance the generative ability of our GR model, we incorporate multi-aspect cross-modal alignments, including the implicit and explicit alignments. Finally, we conduct extensive experiments on three well-known recommendation datasets to demonstrate the effectiveness of our proposed method.

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AAAI Conference 2025 Conference Paper

Mixed-Curvature Multi-Modal Knowledge Graph Completion

Yuxiao Gao
Fuwei Zhang
Zhao Zhang
Xiaoshuang Min
Fuzhen Zhuang

Multi-modal Knowledge Graph Completion (KGC), which aims to enrich knowledge graph embeddings by incorporating images and text as supplementary information alongside triplets, is an significant task in learning KGs. Existing multi-modal KGC methods mainly focus on modalitylevel fusion, neglecting the importance of modeling the complex structures, such as hierarchical and circular patterns. To address this, we propose a Mixed-Curvature multi-modal Knowledge Graph Completion method (MCKGC) that embeds the information into three single-curvature spaces, including hyperbolic space, hyperspherical space, and Euclidean space, and incorporates multi-modal information into a mixed space. Specifically, MCKGC consists of Modality Information Mixed-Curvature Module (MIMCM) and Progressive Fusion Module (PFM). To improve the expressive ability for different modalities, MIMCM introduces multi-modal information into three single-curvature spaces for interaction. Then, to extract useful information from different modalities and capture the complex structure from the geometric information, PFM implements a progressive fusion strategy by utilizing modality-level and space-level gates to adaptively incorporate the information from different spaces. Extensive experiments on three widely used benchmarks demonstrate the effectiveness of our method.

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AAAI Conference 2022 Conference Paper

Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement

Fuwei Zhang
Zhao Zhang
Xiang Ao
Dehong Gao
Fuzhen Zhuang
Yi Wei
Qing He

Cross-Lingual Information Retrieval (CLIR) aims to rank the documents written in a language different from the user’s query. The intrinsic gap between different languages is an essential challenge for CLIR. In this paper, we introduce the multilingual knowledge graph (KG) to the CLIR task due to the sufficient information of entities in multiple languages. It is regarded as a “silver bullet” to simultaneously perform explicit alignment between queries and documents and also broaden the representations of queries. And we propose a model named CLIR with hierarchical knowledge enhancement (HIKE) for our task. The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism. Particularly, HIKE first integrates the entities and their neighborhood in KG into query representations with a knowledge-level fusion, then combines the knowledge from both source and target languages to further mitigate the linguistic gap with a language-level fusion. Finally, experimental results demonstrate that HIKE achieves substantial improvements over state-ofthe-art competitors.

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