Most Influential CIKM Papers (2026-03 Version)

2026-03-27 · Source: Information Retrieval – Resources | Paper Digest · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Advanced, extended

Summary

Paper Digest Team has released the "Most Influential CIKM Papers (2026-03 Version)" on March 27, 2026, providing a ranking of the top 15 papers from each year published at the ACM Conference on Information and Knowledge Management (CIKM). This list is dynamically generated based on citations from both research papers and granted patents, ensuring it reflects the most current impact. The platform also offers services for searching, reviewing, and browsing papers by venue or author, alongside daily digest services and research tools for reading, writing, answering questions, conducting literature reviews, and generating reports. The ranking includes papers from 2025 back to 1993, with recent highlights including "A Content-Driven Micro-Video Recommendation Dataset at Scale" (2025, IF:3) and "BERT4Rec: Sequential Recommendation With Bidirectional Encoder Representations From Transformer" (2019, IF:9).

Key takeaway

For AI Scientists and Research Scientists seeking impactful work in information and knowledge management, regularly consult Paper Digest's dynamic CIKM rankings. This resource helps identify highly cited papers and emerging trends, informing your research direction and potential collaborations. Focus on papers with high 'IF' scores and those cited by patents to pinpoint research with both academic and industrial relevance.

Key insights

Citation-based ranking reveals enduring impact and emerging trends in information and knowledge management research.

Principles

• Influence is quantifiable through citations from research and patents.
• Dynamic ranking reflects evolving research impact over time.

Method

Paper Digest's ranking method automatically analyzes CIKM papers, using citations from both academic publications and granted patents to determine influence, and updates frequently.

In practice

• Utilize Paper Digest's search and review tools for targeted literature discovery.
• Explore top-ranked papers to identify foundational and cutting-edge research.

Topics

• Recommender Systems
• Large Language Models
• Graph Neural Networks
• Time Series Analysis
• Information Retrieval

Best for: AI Scientist, Research Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Information Retrieval – Resources | Paper Digest.