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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)

Paper ID
11750
Venue
VLDB
Year
2018
Pagerank
4.1945683e-05
Overall Rank
11,750 | 18.26%
DOI
10.14778/3157794.3157805

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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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