Crowdsourcing

About: Crowdsourcing is a research topic. Over the lifetime, 12889 publications have been published within this topic receiving 230638 citations.

The Rise of Crowd Logistics: A New Way to Co‐Create Logistics Value

Abstract: Patterned on crowdsourcing and crowdfunding, a new crowd practice has emerged in recent years: crowd logistics. In this paper, we propose a first conceptualization of this growing phenomenon. Crowd logistics is a novel way of providing logistics services that taps into the dormant logistics resources and capabilities of individuals, using mobile applications and web-based platforms. Although crowd logistics has been widely discussed in the business world, it has not yet been the subject of any academic publication. Following an exploratory case study approach, we review the websites of 57 crowd logistics initiatives around the world and highlight the main distinctive characteristics of crowd logistics, as compared to traditional business logistics. We introduce a segmented analysis in which crowd logistics solutions are classified according to four types of service offered. Finally, we introduce six theoretical propositions on the future development of crowd logistics. At a theoretical level, our findings contribute to enriching the service-dominant logic perspective in the logistics field by conceptualizing the crowd as a co-creator of logistics value. At a managerial level, our findings contribute to identifying which types of crowd logistics services are more likely to threaten or disrupt traditional business.

High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility

Abstract: The potential of crowdsourcing for complex problem solving has been revealed by smartphones. Nowadays, vehicles have also been increasingly adopted as participants in crowd-sourcing applications. Different from smartphones, vehicles have the distinct advantage of predictable mobility, which brings new insight into improving the crowdsourcing quality. Unfortunately, utilizing the predictable mobility in participant recruitment poses a new challenge of considering not only current location but also the future trajectories of participants. Therefore, existing participant recruitment algorithms that only use the current location may not perform well. In this paper, based on the predicted trajectory, we present a new participant recruitment strategy for vehicle-based crowdsourcing. This strategy guarantees that the system can perform well using the currently recruited participants for a period of time in the future. The participant recruitment problem is proven to be NP-complete, and we propose two algorithms, a greedy approximation and a genetic algorithm, to find the solution for different application scenarios. The performance of our algorithms is demonstrated with traffic trace dataset. The results show that our algorithms outperform some existing approaches in terms of the crowdsourcing quality.

CrowdMap: crowdsourcing ontology alignment with microtasks

Abstract: The last decade of research in ontology alignment has brought a variety of computational techniques to discover correspondences between ontologies. While the accuracy of automatic approaches has continuously improved, human contributions remain a key ingredient of the process: this input serves as a valuable source of domain knowledge that is used to train the algorithms and to validate and augment automatically computed alignments. In this paper, we introduce CrowdMap, a model to acquire such human contributions via microtask crowdsourcing. For a given pair of ontologies, CrowdMap translates the alignment problem into microtasks that address individual alignment questions, publishes the microtasks on an online labor market, and evaluates the quality of the results obtained from the crowd. We evaluated the current implementation of CrowdMap in a series of experiments using ontologies and reference alignments from the Ontology Alignment Evaluation Initiative and the crowdsourcing platform CrowdFlower. The experiments clearly demonstrated that the overall approach is feasible, and can improve the accuracy of existing ontology alignment solutions in a fast, scalable, and cost-effective manner.

Who are the Turkers? Worker Demographics in Amazon Mechanical Turk

Abstract: Amazon Mechanical Turk (MTurk) is a crowdsourcing system in which tasks are distributed to a population of thousands of anonymous workers for completion. This system is becoming increasingly popular with researchers and developers. In this paper, we survey MTurk workers about their demographic make-up and usage behavior. We find that this population is diverse across several notable demographic dimensions such as age, gender, and income, but is not precisely representative of the U.S. as a whole. Indeed, certain homogeneous aspects of the population, such as education level and nationality, may impose limits on the appropriateness of Turkers as a target community for some interventions or research areas. An awareness of the demographics and behaviors of MTurk workers is important for understanding the capabilities and potential side effects of using this system.

Building Task-Oriented Dialogue Systems for Online Shopping.

Abstract: We present a general solution towards building task-oriented dialogue systems for online shopping, aiming to assist online customers in completing various purchase-related tasks, such as searching products and answering questions, in a natural language conversation manner. As a pioneering work, we show what & how existing NLP techniques, data resources, and crowdsourcing can be leveraged to build such task-oriented dialogue systems for E-commerce usage. To demonstrate its effectiveness, we integrate our system into a mobile online shopping app. To the best of our knowledge, this is the first time that an AI bot in Chinese is practically used in online shopping scenario with millions of real consumers. Interesting and insightful observations are shown in the experimental part, based on the analysis of human-bot conversation log. Several current challenges are also pointed out as our future directions.