activity
20242026
most citedFrom Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.IR2026

Towards Personalized Bangla Book Recommendation: A Large-Scale Heterogeneous Book Graph Dataset

Rahin Arefin Ahmed, Md. Anik Chowdhury, Sakil Ahmed Sheikh Reza +4

Personalized book recommendation in Bangla literature has been constrained by the lack of structured, large-scale, and publicly available datasets. This work introduces RokomariBG,…

cs.IR20261 cited

From Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space

Pengyue Jia, Xiaobei Wang, Yingyi Zhang +14

In modern recommender systems, list-wise reranking serves as a critical phase within the multi-stage pipeline, finalizing the exposed item sequence and directly impacting user sati…

cs.CL2026

FASA: Frequency-aware Sparse Attention

Yifei Wang, Yueqi Wang, Zhenrui Yue +6

The deployment of Large Language Models (LLMs) faces a critical bottleneck when handling lengthy inputs: the prohibitive memory footprint of the Key Value (KV) cache. To address th…

cs.IR2025

Your Causal Self-Attentive Recommender Hosts a Lonely Neighborhood

Yueqi Wang, Zhankui He, Zhenrui Yue +2

In the context of sequential recommendation, a pivotal issue pertains to the comparative analysis between bi-directional/auto-encoding (AE) and uni-directional/auto-regressive (AR)…

cs.IR2024

Transferable Sequential Recommendation via Vector Quantized Meta Learning

Zhenrui Yue, Huimin Zeng, Yang Zhang +2

While sequential recommendation achieves significant progress on capturing user-item transition patterns, transferring such large-scale recommender systems remains challenging due…

cs.IR2024

Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal Recommendation

Yueqi Wang, Zhenrui Yue, Huimin Zeng +2

Despite recent advancements in language and vision modeling, integrating rich multimodal knowledge into recommender systems continues to pose significant challenges. This is primar…