# Literature verification notes

Verification date: 2026-08-14.

1. Azzopardi & Vinay (2008). Retrievability: An Evaluation Measure for Higher Order Information Access Tasks. CIKM 2008, 561-570. DOI 10.1145/1458082.1458157.
2. Chen et al. (2021). Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP. ACL-IJCNLP 2021, 4472-4485. DOI 10.18653/v1/2021.acl-long.345.
3. Zhu et al. (2021). Popularity-Opportunity Bias in Collaborative Filtering. WSDM 2021. DOI 10.1145/3437963.3441820.
4. Penha et al. (2023). Improving Content Retrievability in Search with Controllable Query Generation. The Web Conference 2023, 3182-3192.
5. Kim, Rahimi & Allan (2024). Discovering Biases in Information Retrieval Models Using Relevance Thesaurus as Global Explanation. EMNLP 2024, 19530-19547. DOI 10.18653/v1/2024.emnlp-main.1089.
6. Wang et al. (2025). Item-Centric Exploration for Cold Start Problem. RecSys 2025, 987-990.
7. Wang et al. (2025). Item Level Exploration Traffic Allocation in Large-scale Recommendation Systems. arXiv:2505.09033.
8. Goyal et al. (2026). Masking or Mitigating? Deconstructing the Impact of Query Rewriting on Retriever Biases in RAG. Findings ACL 2026, 8517-8530. DOI 10.18653/v1/2026.findings-acl.414.
9. Chang, Meng & Ganguly (2025). T-Retrievability: A Topic-Focused Approach to Measure Fair Document Exposure in Information Retrieval. CIKM 2025.

Boundary notes:
- Name-Free Discoverability, Match-Weighted Discoverability, Discovery Routing Gap, Discovery Support Ratio, Discoverability Debt, Algorithmic Invisibility, and AMNESS-D are proposed constructs here.
- Classic document retrievability predates this framework and is explicitly credited.
- The known-name/discovery separation is a constructive logical theorem, not an empirical claim that all exact-name-searchable items are undiscoverable.
- The adoption upper bound assumes discovery is a necessary predecessor of adoption.