Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
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Demo Data from SIGIR 2026

Matching scores: Taxonomy · SPECTER2 embeddings · LLM taxonomy · cross-topic distance
Export assignment Export coverage
✓Data 233 submissions · 195 reviewers · 25 area chairs · 2,011 conflicts of interest Submissions Reviewer pool Conflicts
✓Recruitment optional Set the bar, then find the papers and topics the pool covers badly and recruit for them. 426 in the recruitment pipeline · 220 recruited into the pool. Recruitment bar Diagnose & recruit Recruits
✓Matching Scores ready: Taxonomy. Matching Taxonomy
✓Assignment Solved under your options: people per paper, loads, conflicts of interest, country diversity, co-authors. 585 pairs assigned · 114 papers below the minimum. Assignment
233submissions
195reviewers · 220 recruited in total
25area chairs
114papers below min reviewers
0papers with weak affinity
10topics short of reviewers
65mean best-match score

Needs attention

below K A Comparative Study of Users' Information-Seeking Practices Across Search Engines and Generative AI Chatbots 2 of 3 reviewers Recruit
below K Adaptive Rich-kernelized Contrastive Learning for Capacity Enhancement in Collaborative Filtering 2 of 3 reviewers Recruit
below K Agentic Spatio-Temporal Grounding via Collaborative Reasoning 2 of 3 reviewers Recruit
below K An Epistemic Position-Based Click Model: From Interactions to Epistemic Distributions of Relevance and Bias 2 of 3 reviewers Recruit
below K An Eye Tracking Study: Are AI Overviews Changing Search Behavior? 2 of 3 reviewers Recruit
topic short Reinforcement Learning and Off-Policy Recommendation › Others needs 0.8 reviews, the pool can give 0.0 (0%) Recruit
topic short Multi-Behavior Recommendation needs 2.2 reviews, the pool can give 0.1 (2%) Recruit
topic short Generative Retrieval Efficiency needs 1.5 reviews, the pool can give 0.2 (14%) Recruit
topic short LLM-Based Sequential Recommendation needs 5.4 reviews, the pool can give 1.2 (23%) Recruit
topic short Deep Research and Report Generation needs 6.8 reviews, the pool can give 1.6 (23%) Recruit

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