Temporal context-aware task recommendation in crowdsourcing systems

Competitive gamification in crowdsourcing-based contextual-aware recommender systems

“…Similar to Yuen's study (2021) which found that incorporating temporal context information into task recommendation approaches improved the accuracy of task recommendations in crowdsourcing systems, our study also found that context information can improve the effectiveness of restaurant recommendations with the user-generated content from the crowdsourcing system. These findings can inform the development of more effective recommendation systems that consider relevant contextual information.”…”

Data Visualization and Analysis with Machine Learning for the USA’s COVID-19 Prediction

“…With a user-friendly UI, our proposed system is able to help the user to better understand the data related to the COVID-19 more intuitively and easily, and make more informed decisions during the pandemic. In the future, we plan to develop a recommender system [15] for user activities based on the findings in this system.…”

Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development

“…Leveraging 60 features, their model achieved significant improvements, with a precision of 82%, a recall of 84%, and an 81% average reduction in exploration effort over existing approaches. Yuen et al [23] introduced TATaRec, a time-aware task recommendation framework that improves scalability by considering temporal variations in worker preferences. Utilizing workers' social media activities, their approach surpassed previous studies.…”

“…Different M/DL-based strategies are successful in solving different issues in the CCSD domain. For example, recommending the appropriate developer for the required software development project [21,22], investigating the developer's history [23,24], figuring out other contributing success factors of the CCSD projects [14,25], developing simulation methods for failure prediction and task scheduling [26,27], success prediction in the CCSD [4], and quality assessment [28,29]. Authors [30,31] provided significant contributions to this field, specifically in the context of using deep learning for project success prediction.…”