Publications (8)
PHKT:Personalized Dynamic Hypergraph-enhanced KAN-Transformer for Multi-behavior Sequential Recommendation
Ruijie Du, Hao Chen, Xin Zhang +5
In multi-behavior recommendation, auxiliary behaviors such as clicks, add-to-cart, and purchases can provide richer supervisory information for predicting target behaviors. Althoug…
TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendation
Jianqing Zhang, Dongjing Wang, Dongjin Yu
Recently, deep neural networks are widely applied in recommender systems for their effectiveness in capturing/modeling users' preferences. Especially, the attention mechanism in de…
A survey of security and privacy issues in the Internet of Things from the layered context
Samundra Deep, Xi Zheng, Alireza Jolfaei +3
Internet of Things (IoT) is a novel paradigm, which not only facilitates a large number of devices to be ubiquitously connected over the Internet but also provides a mechanism to r…
SkillSelect-Serve: QoS-Aware Budgeted Skill Service Recommendation for LLM Agents
Jingyuan Zheng, Dongjing Wang, Xin Zhang +7
The paper introduces SkillSelect-Serve, a framework that recommends reusable skill services for LLM agents while respecting token, risk, and tool constraints, improving hit rates a…
Is Personalized Modality Weighting Actually Personalized? A Controlled Audit of Per-User Weighting Claims in Multimodal Recommenders
Jingyuan Zheng, Xin Zhang, Yang Gu +6
Per-user modality weighting is deployed at billion-user scale in multimodal recommenders, through user modality-strength vectors, attention gates, meta-weight hypernetworks, and lo…
An ensemble learning approach for software semantic clone detection
Min Fu, Gang Luo, Xi Zheng +3
Code clone is a serious problem in software and has the potential to software defects, maintenance overhead, and licensing violations. Therefore, clone detection is important for r…
NFARec: A Negative Feedback-Aware Recommender Model
Xinfeng Wang, Fumiyo Fukumoto, Jin Cui +2
Graph neural network (GNN)-based models have been extensively studied for recommendations, as they can extract high-order collaborative signals accurately which is required for hig…
CaDRec: Contextualized and Debiased Recommender Model
Xinfeng Wang, Fumiyo Fukumoto, Jin Cui +3
Recommender models aimed at mining users' behavioral patterns have raised great attention as one of the essential applications in daily life. Recent work on graph neural networks (…