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20192026
most citedA Comprehensive Survey on Transfer Learning

227 citations · 461 across the 14 of their papers we have counts for

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Showing cs.IRShow all

5 papers · 1 filter

cs.IR2026

Beyond Matching: Category-Guided Latent Intent Reasoning for Generative Retrieval in E-Commerce

Fuwei Zhang, Xiaoyu Liu, Jiajie Jin +8

Generative retrieval offers a new paradigm for e-commerce search by mapping user queries directly to product Semantic Identifiers (SIDs). However, e-commerce queries are often shor…

cs.IR2025

Multi-Aspect Cross-modal Quantization for Generative Recommendation

Fuwei Zhang, Xiaoyu Liu, Dongbo Xi +5

Generative Recommendation (GR) has emerged as a new paradigm in recommender systems. This approach relies on quantized representations to discretize item features, modeling users'…

cs.IR2024

Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework

Dongbo Xi, Zhen Chen, Yuexian Wang +4

Feed recommendation is currently the mainstream mode for many real-world applications (e.g., TikTok, Dianping), it is usually necessary to model and predict user interests in multi…

cs.IR2021★ 122 cited

Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users

Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang +5

Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich info…

cs.IR2020★ 5 cited

Graph Factorization Machines for Cross-Domain Recommendation

Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu +3

Recently, graph neural networks (GNNs) have been successfully applied to recommender systems. In recommender systems, the user's feedback behavior on an item is usually the result…