Recommendation of Tourism Resources Supported by Crowdsourcing

Context-aware recommendation of personalised tourism resources is possible because of personal mobile devices and powerful data filtering algorithms. The devices contribute with computing capabilities, on board sensors, ubiquitous Internet access and continuous user monitoring, whereas the filtering algorithms provide the ability to match the profile (interests and the context) of the tourist against a large knowledge bases of tourism resources. While, in terms of technology, personal mobile devices can gather user-related information, including the user context and access multiple data sources, the creation and maintenance of an updated knowledge base of tourism-related resources requires a collaborative approach due to the heterogeneity, volume and dynamic nature of the resources. The current PhD thesis aims to contribute to the solution of this problem by adopting a Crowdsourcing approach for the collaborative maintenance of the knowledge base of resources, Trust and Reputation for the validation of uploaded resources as well as publishers, Big Data for user profiling and context-aware filtering algorithms for the personalised recommendation of tourism resources

Accurator: Nichesourcing for Cultural Heritage

With more and more cultural heritage data being published online, their usefulness in this open context depends on the quality and diversity of descriptive metadata for collection objects. In many cases, existing metadata is not adequate for a variety of retrieval and research tasks and more specific annotations are necessary. However, eliciting such annotations is a challenge since it often requires domain-specific knowledge. Where crowdsourcing can be successfully used for eliciting simple annotations, identifying people with the required expertise might prove troublesome for tasks requiring more complex or domain-specific knowledge. Nichesourcing addresses this problem, by tapping into the expert knowledge available in niche communities. This paper presents Accurator, a methodology for conducting nichesourcing campaigns for cultural heritage institutions, by addressing communities, organizing events and tailoring a web-based annotation tool to a domain of choice. The contribution of this paper is threefold: 1) a nichesourcing methodology, 2) an annotation tool for experts and 3) validation of the methodology and tool in three case studies. The three domains of the case studies are birds on art, bible prints and fashion images. We compare the quality and quantity of obtained annotations in the three case studies, showing that the nichesourcing methodology in combination with the image annotation tool can be used to collect high quality annotations in a variety of domains and annotation tasks. A user evaluation indicates the tool is suited and usable for domain specific annotation tasks

Hybrid human-AI driven open personalized education

Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer. In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer). All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result

Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time

Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment with 80 participants and 281 conversations shows that Evorus can automate itself without compromising conversation quality. Crowd-AI architectures have long been proposed as a way to reduce cost and latency for crowd-powered systems; Evorus demonstrates how automation can be introduced successfully in a deployed system. Its architecture allows future researchers to make further innovation on the underlying automated components in the context of a deployed open domain dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human Factors in Computing Systems 2018 (CHI'18