Cheaper and Better: Selecting Good Workers for Crowdsourcing

Crowdsourcing provides a popular paradigm for data collection at scale. We study the problem of selecting subsets of workers from a given worker pool to maximize the accuracy under a budget constraint. One natural question is whether we should hire as many workers as the budget allows, or restrict on a small number of top-quality workers. By theoretically analyzing the error rate of a typical setting in crowdsourcing, we frame the worker selection problem into a combinatorial optimization problem and propose an algorithm to solve it efficiently. Empirical results on both simulated and real-world datasets show that our algorithm is able to select a small number of high-quality workers, and performs as good as, sometimes even better than, the much larger crowds as the budget allows

Recommending places blased on the wisdom-of-the-crowd

The collective opinion of a great number of users, popularly known as wisdom of the crowd, has been seen as powerful tool for solving problems. As suggested by Surowiecki in his books [134], large groups of people are now considered smarter than an elite few, regardless of how brilliant at solving problems or coming to wise decisions they are. This phenomenon together with the availability of a huge amount of data on the Web has propitiated the development of solutions which employ the wisdom-of-the-crowd to solve a variety of problems in different domains, such as recommender systems [128], social networks [100] and combinatorial problems [152, 151]. The vast majority of data on the Web has been generated in the last few years by billions of users around the globe using their mobile devices and web applications, mainly on social networks. This information carries astonishing details of daily activities ranging from urban mobility and tourism behavior, to emotions and interests. The largest social network nowadays is Facebook, which in December 2015 had incredible 1.31 billion mobile active users, 4.5 billion “likes” generated daily. In addition, every 60 seconds 510 comments are posted, 293, 000 statuses are updated, and 136,000 photos are uploaded1. This flood of data has brought great opportunities to discover individual and collective preferences, and use this information to offer services to meet people’s needs, such as recommending relevant and interesting items (e.g. news, places, movies). Furthermore, it is now possible to exploit the experiences of groups of people as a collective behavior so as to augment the experience of other. This latter illustrates the important scenario where the discovery of collective behavioral patterns, the wisdom-of-the-crowd, may enrich the experience of individual users. In this light, this thesis has the objective of taking advantage of the wisdom of the crowd in order to better understand human mobility behavior so as to achieve the final purpose of supporting users (e.g. people) by providing intelligent and effective recommendations. We accomplish this objective by following three main lines of investigation as discussed below. In the first line of investigation we conduct a study of human mobility using the wisdom-of- the-crowd, culminating in the development of an analytical framework that offers a methodology to understand how the points of interest (PoIs) in a city are related to each other on the basis of the displacement of people. We experimented our methodology by using the PoI network topology to identify new classes of points of interest based on visiting patterns, spatial displacement from one PoI to another as well as popularity of the PoIs. Important relationships between PoIs are mined by discovering communities (groups) of PoIs that are closely related to each other based on user movements, where different analytical metrics are proposed to better understand such a perspective. The second line of investigation exploits the wisdom-of-the-crowd collected through user-generated content to recommend itineraries in tourist cities. To this end, we propose an unsupervised framework, called TripBuilder, that leverages large collections of Flickr photos, as the wisdom-of- the-crowd, and points of interest from Wikipedia in order to support tourists in planning their visits to the cities. We extensively experimented our framework using real data, thus demonstrating the effectiveness and efficiency of the proposal. Based on the theoretical framework, we designed and developed a platform encompassing the main features required to create personalized sightseeing tours. This platform has received significant interest within the research community, since it is recognized as crucial to understand the needs of tourists when they are planning a visit to a new city. Consequently this led to outstanding scientific results. In the third line of investigation, we exploit the wisdom-of-the-crowd to leverage recommendations of groups of people (e.g. friends) who can enjoy an item (e.g. restaurant) together. We propose GroupFinder to address the novel user-item group formation problem aimed at recommending the best group of friends for a pair. The proposal combines user-item relevance information with the user’s social network (ego network), while trying to balance the satisfaction of all the members of the group for the item with the intra-group relationships. Algorithmic solutions are proposed and experimented in the location-based recommendation domain by using four publicly available Location-Based Social Network (LBSN) datasets, showing that our solution is effective and outperforms strong baselines

Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

Equity crowdfunding, shareholder structures, and firm performance

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Research question/issue: This paper provides a first-time glimpse into the postcampaign financial and innovative performance of equity-crowdfunded (ECF) and matched nonequity-crowdfunded (NECF) firms. We further investigate how direct and nominee shareholder structures in ECF firms are associated with firm performance. Research findings/insights: We find that ECF firms have 8.5 times higher failure rates than matched NECF firms. However, 3.4 times more ECF firms have patent applications than matched NECF firms. Within the group of ECF firms, we find that ECF firms financed through a nominee structure make smaller losses, whereas ECF firms financed through a direct shareholder structure have more new patent applications, including foreign patent applications. Theoretical/academic implications: Our findings suggest that there are important adverse selection issues on equity crowdfunding platforms, although these platforms also serve as a catalyst for innovative activities. Moreover, our findings suggest that there is a more complex relationship between dispersed versus concentrated crowd shareholders and firm performance than currently assumed in the literature. Practitioner/policy implications: For policy makers and crowdfunding platforms, investor protection against adverse selection will be important to ensure the sustainability of equity crowdfunding markets. For entrepreneurs and crowd investors, our study highlights how equity crowdfunding and the adopted shareholder structure relate to short-term firm performance.Research Foundation—Flander