conf-papers

To find top conference papers for a specific year, query DBLP for venue proceedings and enrich the results with Semantic Scholar data. This process applies a three-dimensional scoring system to rank papers by relevance, popularity, and quality, outputting the top recommendations.

Conference papers are ranked using a three-dimensional composite score: relevance (40%), popularity (40%), and quality (20%). DBLP provides the initial paper listings, while Semantic Scholar enriches the data with citation counts, abstracts, and influential citations to compute the final rankings.

Yes, you can generate Obsidian-ready notes directly from ranked conference papers. The process outputs a top-papers note file and prepares individual deep-dive entries for papers that have corresponding arXiv IDs, integrating seamlessly into your knowledge base.

The best way to filter large lists of conference papers is through a two-stage filtering mechanism. It applies lightweight keyword filtering against DBLP results using configured keywords and exclusions to quickly narrow down candidates before Semantic Scholar enrichment.

Yes, the paper search method supports deep analysis for the top three ranked papers. If these leading papers have available arXiv IDs, the system triggers deeper analysis to generate detailed reports and extract images for comprehensive evaluation.

You need a Python environment with the requests and pyyaml dependencies installed to retrieve conference paper metadata. This setup allows the system to execute DBLP searches, query Semantic Scholar APIs, and process the YAML configurations for scoring and note generation.