ACM Transactions on Information Systems

Papers
(The H4-Index of ACM Transactions on Information Systems is 38. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-09-01 to 2025-09-01.)
ArticleCitations
LkeRec: Toward Lightweight End-to-End Joint Representation Learning for Building Accurate and Effective Recommendation387
Towards Unified Representation Learning for Career Mobility Analysis with Trajectory Hypergraph313
Document-level Relation Extraction via Separate Relation Representation and Logical Reasoning172
Learning Implicit and Explicit Multi-task Interactions for Information Extraction127
Graph Co-Attentive Session-based Recommendation107
eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks105
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls103
User Cold-Start Recommendation via Inductive Heterogeneous Graph Neural Network89
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services73
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks71
Understanding the “Pathway” Towards a Searcher’s Learning Objective69
DiffuRec: A Diffusion Model for Sequential Recommendation68
Learning from Hierarchical Structure of Knowledge Graph for Recommendation67
Efficient Multi-modal Hashing with Online Query Adaption for Multimedia Retrieval63
Efficient and Adaptive Recommendation Unlearning: A Guided Filtering Framework to Erase Outdated Preferences61
SSR: Solving Named Entity Recognition Problems via a Single-stream Reasoner60
Bias and Debias in Recommender System: A Survey and Future Directions58
Users Meet Clarifying Questions: Toward a Better Understanding of User Interactions for Search Clarification55
Revisiting Conversation Discourse for Dialogue Disentanglement50
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions46
How Many Crowd Workers Do I Need? On Statistical Power when Crowdsourcing Relevance Judgments45
ID-centric Pre-training for Recommendation44
Review-Enhanced Universal Sequence Representation Learning for Recommender Systems44
Genomics-Enhanced Cancer Risk Prediction for Personalized LLMs-Driven Healthcare Recommender Systems44
Bottlenecked Heterogeneous Graph Contrastive Learning for Robust Recommendation44
CAFE+: Towards Compact, Adaptive, and Fast Embedding for Large-scale Online Recommendation Models43
H3GNN: Hybrid Hierarchical HyperGraph Neural Network for Personalized Session-based Recommendation43
TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic Recommendation42
A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems41
CaGE: A Causality-inspired Graph Neural Network Explainer for Recommender Systems41
MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation41
Enhancing ID-based Recommendation with Large Language Models40
User Profiling Based on Nonlinguistic Audio Data40
Pre-Trained Models for Search and Recommendation: Introduction to the Special Issue—Part 240
Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count Prediction40
Toward Best Practices for Training Multilingual Dense Retrieval Models39
Collaborative Sequential Recommendations via Multi-view GNN-transformers39
MiDTD: A Simple and Effective Distillation Framework for Distantly Supervised Relation Extraction38
LegalGNN: Legal Information Enhanced Graph Neural Network for Recommendation38
On the User Behavior Leakage from Recommender System Exposure38
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