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  2. Signal Canvas
  3. Aligning Recommendations with User Popularity Preferences
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Aligning Recommendations with User Popularity Preferences

Fresh3d ago
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0.0/10

Compared to this week’s papers

Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T20:55:15.990582+00:00

Claims: 0

References: 95

Proof: unverified

Freshness: fresh

Source paper: Aligning Recommendations with User Popularity Preferences

PDF: https://arxiv.org/pdf/2604.01036v1

Source count: 3

Coverage: 50%

Last proof check: 2026-04-02T20:58:54.056Z

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

Paper Mode

Aligning Recommendations with User Popularity Preferences

Overall score: 7/10
Lineage: 64d35ded85d4…
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Canonical Paper Receipt

Last verification: 2026-04-02T20:58:54.056Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 95

Sources: 3

Coverage: 50%

Missingness
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Unknowns
  • - distribution readiness has not been computed yet
  • - proof verification has not been recorded yet

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  • Paper mode pins trust state to the canonical paper kernel.
  • Workspace mode blends saved sources, prior evidence queries, and linked papers.

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Dimensions overall score 7.0

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Builds On This
Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation
Score 3.0down
Prior Work
Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation
Score 7.0stable
Prior Work
From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review
Score 7.0stable
Higher Viability
Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems
Score 8.0up
Competing Approach
Bridging Semantic Understanding and Popularity Bias with LLMs
Score 7.0stable
Competing Approach
Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring
Score 7.0stable
Competing Approach
Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
Score 4.0down
Competing Approach
Exploring How Fair Model Representations Relate to Fair Recommendations
Score 5.0down

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