Worker Recommendation for Crowdsourced Q&A Services: A Triple-Factor Aware Approach
Summary: A triple-factor-aware recommender jointly models worker expertise, task preferences, and activeness for high-quality crowdsourced Q&A. Latent hierarchical factorization uses positive-only inference, while sampling yields near-optimal batch assignments efficiently. (summarized by gpt-5.6-luna on Jul 24 2026)
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BibTeX Citation
@article{liu_vldb18,
title = {{Worker Recommendation for Crowdsourced Q\&A Services: A Triple-Factor Aware Approach}},
author = {Liu, Zheng and Chen, Lei},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {3},
pages = {380--392},
doi = {10.14778/3157794.3157805},
url = {https://doi.org/10.14778/3157794.3157805},
year = {2018}
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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 |
|---|---|---|---|---|
| 3,904 | QASCA: A Quality-Aware Task Assignment System for Crowdsourcing Applications | 2015 | SIGMOD | 7.0304212e-05 |
| 8,747 | Reliable Diversity-Based Spatial Crowdsourcing by Moving Workers | 2015 | VLDB | 5.3766157e-05 |
| 10,032 | gMission: A General Spatial Crowdsourcing Platform | 2014 | VLDB | 5.1741314e-05 |
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