Ranking Companion integrates six complementary item-selection methods with model-driven active learning in a visual analytics interface to support iterative personalized ranking creation via known-item judgments.
A user-centric evaluation framework for recommender systems,
3 Pith papers cite this work, alongside 161 external citations. Polarity classification is still indexing.
3
Pith papers citing it
161
external citations · external index
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Ocean4Rec uses offline LLM to create OCEAN profiles for items and time-decayed user profiles for request-time numeric reranking, improving NDCG@20 by 7.6% and 61.5% over base+recency in offline VOD evaluations.
A multi-agent multimodal system with fact-grounded adjudication and a dynamic two-tier preference graph cuts false positives in content filtering by 74.3% and nearly doubles F1-score versus text-only baselines while supporting user-driven Delta adjustments.
citing papers explorer
-
Ranking Companion: A Visual Analytics Approach to Item-Based Ranking with Hybrid Item Selection
Ranking Companion integrates six complementary item-selection methods with model-driven active learning in a visual analytics interface to support iterative personalized ranking creation via known-item judgments.
-
Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking
Ocean4Rec uses offline LLM to create OCEAN profiles for items and time-decayed user profiles for request-time numeric reranking, improving NDCG@20 by 7.6% and 61.5% over base+recency in offline VOD evaluations.
-
Transparent and Controllable Recommendation Filtering via Multimodal Multi-Agent Collaboration
A multi-agent multimodal system with fact-grounded adjudication and a dynamic two-tier preference graph cuts false positives in content filtering by 74.3% and nearly doubles F1-score versus text-only baselines while supporting user-driven Delta adjustments.