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20192026
most citedParameterized Knowledge Transfer for Personalized Federated Learning

25 citations · 58 across the 32 of their papers we have counts for

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5 papers · 1 filter

cs.IR2026

Unbiased Rectification for Sequential Recommender Systems Under Fake Orders

Qiyu Qin, Yichen Li, Haozhao Wang +3

Fake orders pose increasing threats to sequential recommender systems by misleading recommendation results through artificially manipulated interactions, including click farming, c…

cs.IR2025

Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation

Yifan Wang, Weinan Gan, Longtao Xiao +7

Generative recommendation (GR) typically encodes behavioral or semantic aspects of item information into discrete tokens, leveraging the standard autoregressive (AR) generation par…

cs.IR2025

A Systematic Survey on Federated Sequential Recommendation

Yichen Li, Qiyu Qin, Gaoyang Zhu +5

Sequential recommendation is an advanced recommendation technique that utilizes the sequence of user behaviors to generate personalized suggestions by modeling the temporal depende…

cs.IR2025

UNGER: Generative Recommendation with A Unified Code via Semantic and Collaborative Integration

Longtao Xiao, Haozhao Wang, Cheng Wang +6

With the rise of generative paradigms, generative recommendation has garnered increasing attention. The core component is the item code, generally derived by quantizing collaborati…

cs.IR2024

Mixed-Precision Embeddings for Large-Scale Recommendation Models

Shiwei Li, Zhuoqi Hu, Xing Tang +6

Embedding techniques have become essential components of large databases in the deep learning era. By encoding discrete entities, such as words, items, or graph nodes, into continu…