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20152026
most citedTemporal Relational Ranking for Stock Prediction

476 citations · 1.3k across the 134 of their papers we have counts for

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58 papers · 1 filter

cs.IR2026

Structure-aware Relative Policy Optimization for Ranking

Yiteng Tu, Weihang Su, Zitao Su +3

Ranking is a fundamental component of modern information access systems. Reinforcement learning (RL) provides a flexible framework for directly optimizing coarse-grained feedback a…

cs.IR2026

SlimPer: Make Personalization Model Slim and Smart

Siqi Wang, Xianjie Chen, Shaofeng Deng +42

Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely o…

cs.IR2026

Generative Chinese Statute Retrieval

Yiteng Tu, Zitao Su, Weihang Su +5

Statute retrieval is a fundamental task in legal information retrieval, yet existing approaches struggle to bridge the gap between colloquial legal queries and formal statutory lan…

cs.IR2026

Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation

Dongqi Fu, Kaushik Rangadurai, Haiyu Lu +13

The increase in data volume, computational resources, and model parameters during training has led to the development of numerous large-scale industrial retrieval models for recomm…

cs.IR2026

Analytical Search

Yiteng Tu, Shuo Miao, Weihang Su +2

Analytical information needs, such as trend analysis and causal impact assessment, are prevalent across various domains including law, finance, science, and much more. However, exi…

cs.IR2026

Learning to Alleviate Familiarity Bias in Video Recommendation

Zheng Ren, Yi Wu, Jianan Lu +4

Modern video recommendation systems aim to optimize user engagement and platform objectives, yet often face structural exposure imbalances caused by behavioral biases. In this work…

cs.IR2026

Multi-Field Tool Retrieval

Yichen Tang, Weihang Su, Yiqun Liu +1

Integrating external tools enables Large Language Models (LLMs) to interact with real-world environments and solve complex tasks. Given the growing scale of available tools, effect…

cs.IR2026

Multi-Source Retrieval and Reasoning for Legal Sentencing Prediction

Junjie Chen, Haitao Li, Qilei Zhang +9

Legal judgment prediction (LJP) aims to predict judicial outcomes from case facts and typically includes law article, charge, and sentencing prediction. While recent methods perfor…

cs.IR2026

Beyond Exposure: Optimizing Ranking Fairness with Non-linear Time-Income Functions

Xuancheng Li, Tao Yang, Yujia Zhou +2

Ranking systems in web search and recommendation allocate attention among items and providers, and therefore need to balance relevance-based effectiveness with provider fairness. E…

cs.IR2026

Equity vs. Equality: Optimizing Ranking Fairness for Tailored Provider Needs

Yiteng Tu, Weihang Su, Shuguang Han +2

Ranking plays a central role in connecting users and providers in Information Retrieval (IR) systems, making provider-side fairness an important challenge. While recent research ha…

cs.IR2025

Generalized Pseudo-Relevance Feedback

Yiteng Tu, Weihang Su, Yujia Zhou +4

Query rewriting is a fundamental technique in information retrieval (IR). It typically employs the retrieval result as relevance feedback to refine the query and thereby addresses…

cs.IR2025

SelfRACG: Enabling LLMs to Self-Express and Retrieve for Code Generation

Qian Dong, Jia Chen, Qingyao Ai +6

Existing retrieval-augmented code generation (RACG) methods typically use an external retrieval module to fetch semantically similar code snippets used for generating subsequent fr…

cs.IR2025

Enhancing LLM-Based Agents via Global Planning and Hierarchical Execution

Junjie Chen, Haitao Li, Jingli Yang +2

Intelligent agent systems based on Large Language Models (LLMs) have shown great potential in real-world applications. However, existing agent frameworks still face critical limita…

cs.IR2025

Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions

Jia Chen, Qian Dong, Haitao Li +9

User-generated content (UGC) communities, especially those featuring multimodal content, improve user experiences by integrating visual and textual information into results (or ite…

cs.IR2025

External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

Mingfu Liang, Xi Liu, Rong Jin +104

Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommenda…

cs.IR2025

Foundations of GenIR

Qingyao Ai, Jingtao Zhan, Yiqun Liu

The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superio…

cs.IR2024

Unsupervised dense retrieval with conterfactual contrastive learning

Haitian Chen, Qingyao Ai, Xiao Wang +3

Efficiently retrieving a concise set of candidates from a large document corpus remains a pivotal challenge in Information Retrieval (IR). Neural retrieval models, particularly den…

cs.IR2024★ 1 cited

A Collaborative Ensemble Framework for CTR Prediction

Xiaolong Liu, Zhichen Zeng, Xiaoyi Liu +13

Recent advances in foundation models have established scaling laws that enable the development of larger models to achieve enhanced performance, motivating extensive research into…

cs.IR2024

MultiBalance: Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System

Yun He, Xuxing Chen, Jiayi Xu +11

In industrial recommendation systems, multi-task learning (learning multiple tasks simultaneously on a single model) is a predominant approach to save training/serving resources an…

cs.IR2024★ 1 cited

InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction

Zhichen Zeng, Xiaolong Liu, Mengyue Hang +25

Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous informati…

cs.IR2024★ 1 cited

Hierarchical Structured Neural Network: Efficient Retrieval Scaling for Large Scale Recommendation

Kaushik Rangadurai, Siyang Yuan, Minhui Huang +12

Retrieval, the initial stage of a recommendation system, is tasked with down-selecting items from a pool of tens of millions of candidates to a few thousands. Embedding Based Retri…

cs.IR2024

STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals

Weihang Su, Yiran Hu, Anzhe Xie +6

Statute retrieval aims to find relevant statutory articles for specific queries. This process is the basis of a wide range of legal applications such as legal advice, automated jud…

cs.IR2024

EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation

Shaorun Zhang, Zhiyu He, Ziyi Ye +4

In recent years, short video platforms have gained widespread popularity, making the quality of video recommendations crucial for retaining users. Existing recommendation systems p…

cs.IR2024

Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval

Haitao Li, You Chen, Zhekai Ge +4

Legal retrieval techniques play an important role in preserving the fairness and equality of the judicial system. As an annually well-known international competition, COLIEE aims t…