Worker Recommendation for Crowdsourced Q&A Services: A Triple-Factor Aware Approach
Summary: Triple-factor aware WR for crowdsourced Q&A: jointly model expertise, preferences, and activeness. Latent Hierarchical Factorization infers task types and workers' latent traits from history with positive-only inference; sampling-based batch recommendation yields near-optimal results, validated on real/synthetic data. (summarized by gpt-5-nano on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,263 | QASCA: A Quality-Aware Task Assignment System for Crowdsourcing Applications | 2015 | SIGMOD | 7.3097573e-05 |
| 8,543 | Reliable Diversity-Based Spatial Crowdsourcing by Moving Workers | 2015 | VLDB | 4.4937074e-05 |
| 9,868 | gMission: A General Spatial Crowdsourcing Platform | 2014 | VLDB | 4.2675549e-05 |
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