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Dongbo Xi

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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 2021 Conference Paper

Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection

Dongbo Xi
Bowen Song
Fuzhen Zhuang
Yongchun Zhu
Shuai Chen
Tianyi Zhang
Yuan Qi
Qing He

With the explosive growth of e-payment industry, online transaction fraud has become one of the biggest challenges for the business. The historical behavior information of users provides rich information for digging into the users’ fraud risk. While considerable efforts have been made in this direction, a long-standing challenge is how to effectively exploit user’s behavioral information and provide explainable prediction results. In fact, the value variations of same field from different events and the interactions of different fields within one event have proven to be strong indicators of fraudulent behaviors. In this paper, we propose the Dual Importanceaware Factorization Machines (DIFM), which exploits the inter- and intra-event information among users’ behavior sequence from dual perspectives, i. e. , field value variations and field interactions simultaneously for fraud detection. The proposed model is deployed in Alipay’s risk management system, which provides real-time fraud detection service for ecommerce platforms. Experimental results on industrial data under various scenarios in the platform clearly demonstrate that our model achieves significant improvements compared with various state-of-the-art baseline models. Moreover, the DIFM could also give an insight into the explanation of the prediction results from dual perspectives.

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AAAI Conference 2019 Conference Paper

Modelling of Bi-Directional Spatio-Temporal Dependence and Users’ Dynamic Preferences for Missing POI Check-In Identification

Dongbo Xi
Fuzhen Zhuang
Yanchi Liu
Jingjing Gu
Hui Xiong
Qing He

Human mobility data accumulated from Point-of-Interest (POI) check-ins provides great opportunity for user behavior understanding. However, data quality issues (e. g. , geolocation information missing, unreal check-ins, data sparsity) in real-life mobility data limit the effectiveness of existing POIoriented studies, e. g. , POI recommendation and location prediction, when applied to real applications. To this end, in this paper, we develop a model, named Bi-STDDP, which can integrate bi-directional spatio-temporal dependence and users’ dynamic preferences, to identify the missing POI check-in where a user has visited at a specific time. Specifically, we first utilize bi-directional global spatial and local temporal information of POIs to capture the complex dependence relationships. Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users’ dynamic preferences. Moreover, the dynamic preferences are transformed into the same space as the dependence relationships to form the final model. Finally, the proposed model is evaluated on three large-scale real-world datasets and the results demonstrate significant improvements of our model compared with state-of-the-art methods. Also, it is worth noting that the proposed model can be naturally extended to address POI recommendation and location prediction tasks with competitive performances.

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