Towards an Effective Crowdsourcing Recommendation System: A Survey of the State-of-the-Art

Aldhahri, Eman; Shandilya, Vivek; Shiva, Sajjan G.

doi:10.1109/sose.2015.53

Cited by 25 publications

(11 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, it is difficult to determine the pros and cons of the task assignment [35], [36]. The utility of the proposed method is based on the accuracy of the worker-task matching measurement.…”

Section: Discussionmentioning

confidence: 99%

Crowdsourcing Software Task Assignment Method for Collaborative Development

Zhou

Wang

2019

IEEE Access

View full text Add to dashboard Cite

Software crowdsourcing is an emerging and promising software development model. It is based on the characteristics of Internet community intelligence, which makes it have certain advantages in development cost and product quality. Companies are increasingly using crowdsourcing to accomplish specific software development tasks. However, this development model still faces many challenges. One of the key issues is the collaboration between crowdsourced workers. Developer collaboration is important to software development, but workers in crowdsourcing come from an undefined network community, so it's hard to guarantee that they can work together. This paper focuses on task assignment and uses the active time of workers as the basis of grouping to provide a solution for multi-task to multi-worker allocation. Based on the on-demand distribution model, this paper considers three factors: worker's ability, task module complexity, and worker's active time. First, the workers are divided into multiple collaborative candidate groups based on active time. Then, the Hungarian algorithm is used to select the optimal workers for each module from the collaborative candidate groups of each task, and the coordination candidate group replacement strategy is used to solve the assignment failure problem. Finally completing the assignment of all tasks within an assignment cycle. The experiments have shown that the proposed method increases the total utility by 25% and the success rate of distribution by 30% than the sequential assignment method. The proposed method can give a reasonable solution for software crowdsourcing task allocation based on collaborative development. INDEX TERMS Crowdsourcing, software development, collaborative candidate group, Hungarian algorithm, active time.

show abstract

Section: Discussionmentioning

confidence: 99%

Crowdsourcing Software Task Assignment Method for Collaborative Development

Zhou

Wang

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…"A crowdsourcing paradigm facilitates outsourcing tasks to a pool of workers distributed around the world" [94]. While there is a vast amount of posted tasks each second, it is an important task to find the best users which are suitable for this task and satisfy them with good rewards.…”

Section: Crowdsourcing Recommendersmentioning

confidence: 99%

Towards Cognitive Recommender Systems

et al. 2020

View full text Add to dashboard Cite

Intelligence is the ability to learn from experience and use domain experts’ knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts’ knowledge and experience, as it is vital to know the domain that the items will be recommended. Traditionally, Recommender Systems have been recognized as playlist generators for video/music services (e.g., Netflix and Spotify), e-commerce product recommenders (e.g., Amazon and eBay), or social content recommenders (e.g., Facebook and Twitter). However, Recommender Systems in modern enterprises are highly data-/knowledge-driven and may rely on users’ cognitive aspects such as personality, behavior, and attitude. In this paper, we survey and summarize previously published studies on Recommender Systems to help readers understand our method’s contributions to the field in this context. We discuss the current limitations of the state of the art approaches in Recommender Systems and the need for our new approach: A vision and a general framework for a new type of data-driven, knowledge-driven, and cognition-driven Recommender Systems, namely, Cognitive Recommender Systems. Cognitive Recommender Systems will be the new type of intelligent Recommender Systems that understand the user’s preferences, detect changes in user preferences over time, predict user’s unknown favorites, and explore adaptive mechanisms to enable intelligent actions within the compound and changing environments. We present a motivating scenario in banking and argue that existing Recommender Systems: (i) do not use domain experts’ knowledge to adapt to new situations; (ii) may not be able to predict the ratings or preferences a customer would give to a product (e.g., loan, deposit, or trust service); and (iii) do not support data capture and analytics around customers’ cognitive activities and use it to provide intelligent and time-aware recommendations.

show abstract

“…Spatial crowdsourcing (SC) is composed of a crowdsourcing platform, task publisher and workers. The task of SC platform is mainly to find the suitable workers to complete the task, with the goal of improving task coverage and commission aggregation; task publisher finds the suitable workers to complete the spatial task of its release with high quality through the platform with low time and cost; workers need to efficiently find suitable and capable tasks to obtain rewards and improve credibility [18]. At present, most task publishers publish their tasks on the spatial crowdsourcing platform, and the platform assigns tasks according to the characteristics of tasks and workers.…”

Section: Relevant Definitions and Problem Description A Relevantmentioning

confidence: 99%

Method for Spatial Crowdsourcing Task Assignment Based on Integrating of Genetic Algorithm and Ant Colony Optimization

Wang

Zhao

2020

IEEE Access

View full text Add to dashboard Cite

With the rapid development of mobile networks and the proliferation of mobile devices, Spatial Crowdsourcing (SC) has attracted the interest of industry and research groups. In addition to considering the specific spatial constraints in the existing research spatial crowdsourcing, each task has an effective duration, operational complexity, number of workers required, and incentive budget constraints. In this scenario, we studied the MQC-TA (Maximum Quality and Minimum Cost Task Assignment) problem. Firstly, the worker incentive model is established. The MQC-GAC algorithm is designed according to the MQC-TA problem to maximize the task completion quality and minimize the incentive budget. The algorithm combined the fast convergence of Genetic Algorithm and the positive feedback mechanism of Ant Colony Optimization Algorithm. Finally, the effectiveness and efficiency of the proposed method are verified by a comprehensive experiment on the data set. INDEX TERMS Spatial crowdsourcing, task assignment, MQC-TA problem, MQC-GAC algorithm.

show abstract

Towards an Effective Crowdsourcing Recommendation System: A Survey of the State-of-the-Art

Cited by 25 publications

References 19 publications

Crowdsourcing Software Task Assignment Method for Collaborative Development

Crowdsourcing Software Task Assignment Method for Collaborative Development

Towards Cognitive Recommender Systems

Method for Spatial Crowdsourcing Task Assignment Based on Integrating of Genetic Algorithm and Ant Colony Optimization

Contact Info

Product

Resources

About