Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Recomendation systems and crowdsourcing: a good wedding for enabling innovation? Results from technology affordances and costraints theory

Recommendation Systems have come a long way since their first appearance in the e-commerce platforms.Since then, evolved Recommendation Systems have been successfully integrated in social networks. Now its time to test their usability and replicate their success in exciting new areas of web -enabled phenomena. One of these is crowdsourcing. Research in the IS field is investigating the need, benefits and challenges of linking the two phenomena. At the moment, empirical works have only highlighted the need to implement these techniques for tasks assignment in crowdsourcing distributed work platforms and the derived benefits for contributors and firms. We review the variety of the tasks that can be crowdsourced through these platforms and theoretically evaluate the efficiency of using RS to recommend a task in creative crowdsourcing platforms. Adopting a Technology Affordances and Constraints Theory, an emerging perspective in the Information Systems (IS) literature to understand technology use and consequences, we anticipate the tensions that this implementation can generate

YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset, a freely-available collections of more than 50,000 YouTube comments and metadata below autonomous vehicle (AV)-related videos. We describe its creation process, its content and data format, and discuss its possible usages. Especially, we do a case study of the first self-driving car fatality to evaluate the dataset, and show how we can use this dataset to better understand public attitudes toward self-driving cars and public reactions to the accident. Future developments of the dataset are also discussed.Comment: in Proceedings of the Thirteenth International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2018

Using Social Media to Promote STEM Education: Matching College Students with Role Models

STEM (Science, Technology, Engineering, and Mathematics) fields have become increasingly central to U.S. economic competitiveness and growth. The shortage in the STEM workforce has brought promoting STEM education upfront. The rapid growth of social media usage provides a unique opportunity to predict users' real-life identities and interests from online texts and photos. In this paper, we propose an innovative approach by leveraging social media to promote STEM education: matching Twitter college student users with diverse LinkedIn STEM professionals using a ranking algorithm based on the similarities of their demographics and interests. We share the belief that increasing STEM presence in the form of introducing career role models who share similar interests and demographics will inspire students to develop interests in STEM related fields and emulate their models. Our evaluation on 2,000 real college students demonstrated the accuracy of our ranking algorithm. We also design a novel implementation that recommends matched role models to the students.Comment: 16 pages, 8 figures, accepted by ECML/PKDD 2016, Industrial Trac

Implementation of a Training Courses Recommender System based on k-means algorithm

Providing the right professional training courses for employees is a critical issue for organizations as well as employees. Its necessity stemmed out on the fulfillment of the organization and employees need. Thus, building a recommender system that would help in the decision making process and planning of the training course offered by organizations. This can be performed using various techniques and methodologies, where the most important one is data mining. Data mining is a process of looking for specific patterns and knowledge from large databases and carrying out predictions for outputs. Therefore, this project aims to build a web-based application for predicting appropriate training recommenders for Princess Norah University employees based on their education and professional information. This helps the university in suggesting the most optimal training recommender for employees, which in turn can enhance their performance and develop their career and working levels. Employees’ data was gathered from the Human Resource of the university and then clustered using the WEKA program to find the centroids of clusters to be then used in the developed application. The developed web-based application is used to suggest the most suitable training recommender for each employee. Results demonstrate that the developed web-based application effectively suggests the most appropriate training courses for employees based on the previously taken courses, evaluation of courses and probability for promotion. Furthermore, this web-based application can be used for describing the appropriate training courses for new employees based on their levels. The achieved accuracy of the developed system was 73.33%

How open are journalists on Twitter? Trends towards the end-user journalism

The many activities of journalists on Twitter should be analyzed. Are they doing a different kind of journalism? With a content analysis of 1125 tweets, this study reveals trends of some Spanish journalists using Twitter. A traditional role like gatekeeping can be highly amplified in terms of transparency and accountability with actions as retweeting or linking. The landscape offered by this platform is framed with the "ambient journalism", which will help to understand the proposal of this study: the end-user journalism. The findings will show the level of opening with the audience in aspects about replies, requests and linking

Professional online networking : investigating the technological and the human side of networking with professional social networking sites

Professional social networking sites (SNS) have become a vital part of modern days professional lives. They are a convenient way to receive information about job offers, work-related content, and to connect with other professionals independent of time and space. Research in the field of social capital has shown that a network of people can give access to information, influence, and solidarity which positively affect both subjective and objective career outcomes. Moreover, research has shown that a diverse network is most beneficial as it gives access to non-redundant information, new perspectives, and new ideas. Yet, most professional SNS users are mainly connected with others from their direct work environments such as colleagues and university friends. For one thing, this is because of the homophily principle which states that people tend to surround themselves with others who are similar to them. On the other hand, contact recommender systems of professional SNS support connecting with similar others as contact recommendations are usually based on similarity. The cumulative dissertation, therefore, was set out to investigate the technological and the human side of professional online networking to gain evidence on how to encourage professional SNS users to build more diverse business networks. The dissertation consists of four research articles answering the following four research questions: 1. Is there a difference between offline and online professional networking in terms of intensity and in terms of influence factors? 2. How do basic technological features and functions (e.g. diverse contact recommendations) influence professional online networking? 3. How do different information designs of contact recommendations influence professional online networking? 4. How does diverse online networking influence peoples social identification with their online business networks? In summary, the four research articles show that peoples online networking is mainly driven by cognitive factors, more specifically, peoples knowledge about the benefits of (diverse) networking. When people know about the benefits of networking and the benefits of diverse networking, they network more and more diverse. This can be addressed in the design of contact recommendations by displaying an explanation why someone is recommended thereby hinting at the benefits of networking in general and at the benefits of diversity. Moreover, this can be addressed by presenting contact recommendations emphasizing dissimilarity information in contrast to similarity information. Both different types of explanations and different types of information weaken the homophily principle and encourage people to network more diverse. Besides, basic technological functions influence online networking. When people are presented with a more diverse set of contact recommendations to choose from, they do not network less but consequently, end up with a more diverse business network. Furthermore, the negative affective influence of anxiety towards unknown people is different for offline than for online networking. In line with the social compensation hypothesis, in online settings, the negative influence is weaker than it is in offline settings. When only looking at online settings we see that higher levels of anxiety still reduce the number of people connected with but not the diversity of the resulting networks. Hence, people do not feel less anxiety when connecting with similar others than when connecting with dissimilar others. Finally, returning to the side of the user we see that more diverse online networking leads to a reduction of social identification with peoples online business networks. Diverse online networking reduces social identification with the network and as a result the willingness to support the network. Hence, diverse online networking compromises the benefits a network provides. Yet, in the absence of similarity, there is also evidence that people attribute others in their online networks with characteristics of their own to perceive them as similar. Shared characteristics function as a reason to identify and compensate for the lack of formal similarity when business networks become more diverse. Moreover, the specific features and functions of professional SNS besides contact recommendations can compensate for the lack of identification.Berufliche Social Networking Sites (SNS) sind aus dem modernen Berufsleben nicht mehr wegzudenken. Sie sind eine bequeme Möglichkeit, Informationen über Stellenangebote und arbeitsbezogene Inhalte zu erhalten und sich mit Fachleuten unabhängig von Zeit und Raum zu vernetzen. Forschung auf dem Gebiet des sozialen Kapitals hat gezeigt, dass ein Netzwerk Zugang zu Informationen, Einfluss und Solidarität bietet, was sowohl subjektive als auch objektive berufliche Ergebnisse positiv beeinflusst. Darüber hinaus hat die Forschung gezeigt, dass ein diverses Netzwerk am vorteilhaftesten ist, da es den Zugang zu nicht redundanten Informationen, neuen Perspektiven und neuen Ideen ermöglicht. Dennoch sind die meisten Nutzer*innen auf beruflichen SNS hauptsächlich mit anderen aus ihrem direkten Arbeitsumfeld, wie zum Beispiel mit Kolleg*innen und Freund*innen von der Universität vernetzt. Dies liegt zum einen am Homophilie-Prinzip, das besagt, dass Menschen dazu neigen, sich mit Personen zu umgeben, die ihnen ähnlich sind. Zum anderen unterstützen Kontaktempfehlungssysteme auf beruflichen SNS das Vernetzen mit ähnlichen Personen, da Kontaktempfehlungen in der Regel auf Ähnlichkeit basieren. Die kumulative Dissertation untersuchte daher die technologische und die menschliche Seite des beruflichen online Networkings, um Erkenntnisse darüber zu gewinnen, wie Nutzer*innen von beruflichen SNS dazu ermutigt werden können, diverse berufliche Netzwerke aufzubauen. Die Dissertation besteht aus vier Forschungsartikeln, die die folgenden vier Forschungsfragen beantworten: 1. Gibt es einen Unterschied zwischen offline und online beruflichem Networking in Bezug auf die Intensität und in Bezug auf die Einflussfaktoren? 2. Wie beeinflussen grundlegende technologische Merkmale und Funktionen (z.B. diverse Kontaktempfehlungen) das berufliche online Networking? 3. Wie beeinflussen unterschiedliche Informationsdesigns von Kontaktempfehlungen das berufliche online Networking? 4. Wie beeinflusst diverses online Networking die soziale Identifikation der Menschen mit ihren beruflichen online Netzwerken? Zusammenfassend zeigen die vier Artikel, dass online Networking hauptsächlich durch kognitive Faktoren gelenkt wird, genauer gesagt durch das Wissen um die Vorteile von Networking. Wenn Menschen die Vorteile des Networkings und die Vorteile des diversen Networkings kennen, vernetzen sie sich mit mehr Personen und diverser. Dem kann bei der Gestaltung von Kontaktempfehlungen dadurch Rechnung getragen werden, dass eine Erklärung angezeigt wird, warum jemand empfohlen wird. Darüber hinaus kann dem Einfluss des Wissens durch die Auswahl der Informationen von Kontaktempfehlungen Rechnung getragen werden. Bei der Präsentation von Kontaktempfehlungen können Informationen zu Unterschiedlichkeiten im Gegensatz zu Informationen zu Ähnlichkeiten betont werden. Sowohl unterschiedliche Arten von Erklärungen als auch unterschiedliche Arten von Informationen schwächen das Homophilie-Prinzip und ermutigen Nutzer*innen dazu, sich diverser zu vernetzen. Außerdem beeinflussen grundlegende technologische Funktionen das online Networking. Wird ein diverses Set an Kontaktempfehlungen zur Auswahl angeboten, vernetzen sich Nutzer*innen nicht mit weniger Menschen, sondern erhalten ein diverseres Netzwerk. Darüber hinaus ist der negative affektive Einfluss der Angst gegenüber unbekannten Personen beim offline Networking anders als beim online Networking. In Übereinstimmung mit der Hypothese der sozialen Kompensation ist der negative Einfluss in online Umgebungen schwächer als in offline Umgebungen. Wenn wir nur online Networking betrachten, stellen wir fest, dass ein höheres Level an Angst zwar die Größe allerdings nicht die Diversität des entstandenen Netzwerks reduziert. Daraus folgt, dass Menschen nicht weniger Angst empfinden, wenn sie sich mit ähnlichen Personen vernetzen als wenn sie sich mit unähnlichen Personen vernetzen. Wenn wir schließlich auf die Seite der Nutzer*innen zurückkehren, sehen wir, dass diverses online Networking zu einer Verringerung der sozialen Identifikation mit dem beruflichen online Netzwerk führt. Diverses online Networking reduziert die soziale Identifikation mit dem Netzwerk und infolgedessen die Bereitschaft das Netzwerk zu unterstützen. Daher beeinträchtigt diverses online Networking die Vorteile, die ein Netzwerk bietet. Bei fehlender Ähnlichkeit gibt es jedoch auch Hinweise darauf, dass Menschen anderen in ihrem online Netzwerk eigene Eigenschaften und Merkmale zuschreiben, um sie als ähnlich wahrzunehmen. Gemeinsame Eigenschaften und Merkmale dienen als Grundlage, sich mit anderen Personen zu identifizieren und den Mangel an formalen Ähnlichkeiten auszugleichen, wenn berufliche Netzwerke stets diverser werden. Darüber hinaus gleichen auch die spezifischen Merkmale und Funktionen beruflicher SNS, die neben Kontaktempfehlungen existieren, einen Mangel an Identifikation aus