OOIR: Observatory of International Research

Papers

(The H4-Index of ACM Transactions on Information Systems is 41. 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 2022-01-01 to 2026-01-01.)

Article	Citations
Towards Unified Representation Learning for Career Mobility Analysis with Trajectory Hypergraph	744
Periodic Graph Neural Networks for Click-Through Rate Prediction in Online Advertising	362
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services	229
Learning Implicit and Explicit Multi-task Interactions for Information Extraction	157
Understanding the “Pathway” Towards a Searcher’s Learning Objective	124
Learning from Hierarchical Structure of Knowledge Graph for Recommendation	121
SSD4Rec: A Structured State Space Duality Model for Efficient Sequential Recommendation	102
eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks	92
Document-level Relation Extraction via Separate Relation Representation and Logical Reasoning	91
User Cold-Start Recommendation via Inductive Heterogeneous Graph Neural Network	87
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls	84
DiffuRec: A Diffusion Model for Sequential Recommendation	81
CAFE+: Towards Compact, Adaptive, and Fast Embedding for Large-scale Online Recommendation Models	77
How Many Crowd Workers Do I Need? On Statistical Power when Crowdsourcing Relevance Judgments	73
TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic Recommendation	71
Genomics-Enhanced Cancer Risk Prediction for Personalized LLM-Driven Healthcare Recommender Systems	69
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions	67
SSR: Solving Named Entity Recognition Problems via a Single-stream Reasoner	63
MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation	63
ID-centric Pre-training for Recommendation	57
Efficient and Adaptive Recommendation Unlearning: A Guided Filtering Framework to Erase Outdated Preferences	57
H3GNN: Hybrid Hierarchical HyperGraph Neural Network for Personalized Session-based Recommendation	57
Bottlenecked Heterogeneous Graph Contrastive Learning for Robust Recommendation	54
Revisiting Conversation Discourse for Dialogue Disentanglement	52
A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems	51

Users Meet Clarifying Questions: Toward a Better Understanding of User Interactions for Search Clarification	51
Review-Enhanced Universal Sequence Representation Learning for Recommender Systems	51
Bias and Debias in Recommender System: A Survey and Future Directions	50
A Review Selection Method Based on Consumer Decision Phases in E-commerce	48
Pre-Trained Models for Search and Recommendation: Introduction to the Special Issue—Part 2	48
A Revisiting Study of Appropriate Offline Evaluation for Top- N Recommendation Algorithms	47
Toward Best Practices for Training Multilingual Dense Retrieval Models	46
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network	45
MiDTD: A Simple and Effective Distillation Framework for Distantly Supervised Relation Extraction	45
Enhancing ID-based Recommendation with Large Language Models	45
Listwise Generative Retrieval Models via a Sequential Learning Process	44
Beyond Texts: Incorporating Co-occurrences into the Review-based Conversation Recommendation Systems	44
Relieving Popularity Bias in Interactive Recommendation: A Diversity-Novelty-Aware Reinforcement Learning Approach	44
On the User Behavior Leakage from Recommender System Exposure	44
CaGE: A Causality-inspired Graph Neural Network Explainer for Recommender Systems	42
A Consistent Dual-MRC Framework for Emotion-cause Pair Extraction	41
Learning Discrete Identifiers and Dense Vectors for Generative Retrieval	41