Representing Entity Importance in AI Knowledge Systems: A Dual-Signal Framework of Audience Evaluation and Structural Authority

Entity-aware document retrieval uses query-associated entities as ranking signals, assuming that semantically relevant entities are also useful retrieval signals. We show this assumption is insufficient- and explain why. Unlike terms, which are ground-truth observations, entity links are hypotheses produced by an imperfect linker: an entity can be topically central yet provide no discriminative signal if the linker fires indiscriminately across relevant and non-relevant documents. We formalize this as a distinction between Conceptual Entity Relevance (CER)- whether an entity is topically related to a query- and Observable Entity Relevance (OER)- whether its observed presence in a collection discriminates relevant from non-relevant documents. Across four collections and annotation sources including human entity judgments, CER and OER exhibit near-chance agreement ($κ\approx 0$), while OER operationalizations agree substantially ($κ\approx 0.5$), confirming CER as the systematic outlier. CER-based supervision selects topically plausible but weakly discriminative entities, pruning fewer than 4% of non-relevant documents on some collections. Aligning supervision with OER improves non-relevant pruning by up to 10x and open-world MAP by 0.051 over BM25. Our findings motivate a shift from conceptual to observable notions of entity relevance in entity-aware retrieval.

Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs. While Large Language Models show promise as automated assessors, current approaches rely on flat aggregation strategies: single-step prompting, voting ensembles, or uncoordinated multi-agent pipelines that aggregate independent outputs without integration. We propose HIERA, a hierarchical multi-agent relevance assessment framework with four specialized agents: a Relevance Judge, Query Analyzer, Item Analyzer, and Relation Analyzer. The Judge determines when specialist analysis is needed; the Relation Analyzer then coordinates query and item analyses with external knowledge to establish relevance relationships for final judgment. Ablation studies show that the same agents and external knowledge without hierarchical coordination degrade performance, confirming that the coordination structure itself accounts for the improvement. Evaluation across five datasets (EVS, MSRD, ESCI, WANDS, Home Depot) shows improvements over 11 baselines: 10.2% on Home Depot, 4.8% on ESCI, and up to 38% on EVS ($p < 0.05$). Hierarchical coordination yields 12.7% improvement over uncoordinated collaboration using identical agents.

Modern recommendation systems largely infer user preferences from implicit behaviors such as clicks, watch time, and negative feedback, but these signals reveal what users do rather than why they like or dislike content. This work studies articulated user rationales (AURs), i.e., users' natural-language explanations of their preferences, as a new class of polarity-aware and reason-level textual signals for recommendation. Despite their potential value, AURs are difficult to use in industrial systems because they are naturally sparse, often low-quality, and only cover a small fraction of items. We present SARA (Scaling Articulated Rationales), an industrial framework that turns sparse AURs into scalable recommendation signals. SARA first builds a data engine that elicits and curates AURs from 240M Kuaishou Live users, producing SARA-HQ, a quality-controlled and author-centric rationale dataset. It then aligns a general-purpose MLLM into SARA-7B through large-scale SFT and Quality-Refining DPO, extending rationale generation from 86,564 AUR-covered authors to the full 10M-author space. Finally, SARA-Ranker integrates the generated positive and negative rationales into production ranking via rationale-aware interaction modeling and rejection-memory modeling. Extensive offline evaluation, human calibration, and online A/B tests show that SARA-7B generates more specific, polarity-consistent, and grounded rationales than strong MLLM baselines, while SARA-Ranker improves engagement and reduces negative feedback in production. Deployed with daily refresh for over 30 days, SARA establishes articulated rationales as a practical, first-class textual signal for industrial recommendation systems.