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Xiang Wang

5 papers hereh-index 224 citations5 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author3

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.IR4
  • cs.MA1
same name
  • Xiang Wang — 34 papers, h 21
  • Xiang Wang — 27 papers, h 14
  • Xiang Wang — 25 papers, h 15
  • Xiang Wang — 24 papers, h 15
  • Xiang Wang — 22 papers, h 9
  • Xiang Wang — 21 papers, h 35

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedReinforced Preference Optimization for Recommendation

2 citations · 4 across the 5 of their papers we have counts for

collaborators
Showing cs.IRShow all

4 papers · 1 filter

cs.IR2026

Fine-grained Semantics Integration for Large Language Model-based Recommendation

Jiawei Feng, Xiaoyu Kong, Leheng Sheng +8

Recent advances in Large Language Models (LLMs) have driven a shift in recommender systems from the discriminative paradigm to the LLM-based generative paradigm, where the recommen…

cs.IR2025★ 1 cited

MiniOneRec: An Open-Source Framework for Scaling Generative Recommendation

Xiaoyu Kong, Leheng Sheng, Junfei Tan +5

The recent success of large language models (LLMs) has renewed interest in whether recommender systems can achieve similar scaling benefits. Conventional recommenders, dominated by…

cs.IR2025★ 1 cited

Think before Recommendation: Autonomous Reasoning-enhanced Recommender

Xiaoyu Kong, Junguang Jiang, Bin Liu +6

The core task of recommender systems is to learn user preferences from historical user-item interactions. With the rapid development of large language models (LLMs), recent researc…

cs.IR2025★ 2 cited

Reinforced Preference Optimization for Recommendation

Junfei Tan, Yuxin Chen, An Zhang +7

Recent breakthroughs in large language models (LLMs) have fundamentally shifted recommender systems from discriminative to generative paradigms, where user behavior modeling is ach…

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