People-Powered Music: Using User-Generated Tags and Structure in Recommendations

Music recommenders often rely on experts to classify song facets like genre and mood, but user-generated folksonomies hold some advantages over expert classifications—folksonomies can reflect the same real-world vocabularies and categorizations that end users employ. We present an approach for using crowd-sourced common sense knowledge to structure user-generated music tags into a folksonomy, and describe how to use this approach to make music recommendations. We then empirically evaluate our “people-powered” structured content recommender against a more traditional recommender. Our results show that participants slightly preferred the unstructured recommender, rating more of its recommendations as “perfect” than they did for our approach. An exploration of the reasons behind participants’ ratings revealed that users behaved differently when tagging songs than when evaluating recommendations, and we discuss the implications of our results for future tagging and recommendation approaches

Guided generation of pedagogical concept maps from the Wikipedia

We propose a new method for guided generation of concept maps from open accessonline knowledge resources such as Wikies. Based on this method we have implemented aprototype extracting semantic relations from sentences surrounding hyperlinks in the Wikipedia’sarticles and letting a learner to create customized learning objects in real-time based oncollaborative recommendations considering her earlier knowledge. Open source modules enablepedagogically motivated exploration in Wiki spaces, corresponding to an intelligent tutoringsystem. The method extracted compact noun–verb–noun phrases, suggested for labeling arcsbetween nodes that were labeled with article titles. On average, 80 percent of these phrases wereuseful while their length was only 20 percent of the length of the original sentences. Experimentsindicate that even simple analysis algorithms can well support user-initiated information retrievaland building intuitive learning objects that follow the learner’s needs.Peer reviewe

The state of research on folksonomies in the field of Library and Information Science : a Systematic Literature Review

Purpose – The purpose of this thesis is to provide an overview of all relevant peer-reviewed articles on folksonomies, social tagging and social bookmarking as knowledge organisation systems within the field of Library and Information Science by reviewing the current state of research on these systems of managing knowledge. Method – I use the systematic literature review method in order to systematically and transparently review and synthesise data extracted from 39 articles found through the discovery system LUBsearch in order to find out which, and to which degree different methods, theories and systems are represented, which subfields can be distinguished, how present research within these subfields is and which larger conclusions can be drawn from research conducted between 2003-2013 on folksonomies. Findings – There have been done many studies which are exploratory or reviewing literature discussions, and other frequently used methods which have been used are questionnaires or surveys, although often in conjunction with other methods. Furthermore, out of the 39 studies, 22 were quantitative, 15 were qualitative and 2 used mixed methods. I also found that there were an underwhelming number of theories being explicitly used, where merely 11 articles explicitly used theories, and only one theory was used twice. No key authors on the topic were identified, though Knowledge Organization, Information Processing & Management and Journal of the American Society for Information Science and Technology were recognised as key journals for research on folksonomies. There have been plenty of studies on how tags and folksonomies have effected other knowledge organisation systems, or how pre-existing have been used to create new systems. Other well represented subfields include studies on the quality or characteristics of tags or text, and studies aiming to improve folksonomies, search methods or tags. Value – I provide an overview on what has been researched and where the focus on said research has been during the last decade and present future research suggestions and identify possible dangers to be wary of which I argue will benefit folksonomies and knowledge organisation as a whole

MC2: MPEG-7 content modelling communities

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe use of multimedia content on the web has grown significantly in recent years. Websites such as Facebook, YouTube and Flickr cater for enormous amounts of multimedia content uploaded by users. This vast amount of multimedia content requires comprehensive content modelling otherwise retrieving relevant content will be challenging. Modelling multimedia content can be an extremely time consuming task that may seem impossible particularly when undertaken by individual users. However, the advent of Web 2.0 and associated communities, such as YouTube and Flickr, has shown that users appear to be more willing to collaborate in order to take on enormous tasks such as multimedia content modelling. Harnessing the power of communities to achieve comprehensive content modelling is the primary focus of this research. The aim of this thesis is to explore collaborative multimedia content modelling and in particular the effectiveness of existing multimedia content modelling tools, taking into account the key development challenges of existing collaborative content modelling research and the associated modelling tools. Four research objectives are pursued in order to achieve this; first, design a user experiment to study users’ tagging behaviour with existing multimedia tagging tools and identify any relationships between such user behaviour; second, design and develop a framework for MPEG-7 content modelling communities based on the results of the experiment; third, implement an online service as a proof of concept of the framework; fourth, validate the framework through the online service during a repeat of the initial user experiment. This research contributes first, a conceptual model of user behaviour visualised as a fuzzy cognitive map and, second, an MPEG-7 framework for multimedia content modelling communities (MC2) and its proof of concept as an online service. The fuzzy cognitive model embodies relationships between user tagging behaviour and context and provides an understanding of user priorities in the description of content features and the relationships that exist between them. The MC2 framework, developed based on the fuzzy cognitive model, is deep-rooted in user content modelling behaviour and content preferences. A proof of concept of the MC2 framework is implemented as an online service in which all metadata is modelled using MPEG-7. The online service is validated, first, empirically with the same group of users and through the same experiment that led to the development of the fuzzy cognitive model and, second, functionally against the folksonomy and MPEG-7 content modelling tools used in the initial experiment. The validation demonstrates that MC2 has the advantages without the shortcomings of existing multimedia tagging tools by harnessing the ease of use of folksonomy tools while producing comprehensive structured metadata.Supported by UK Engineering and Physical Sciences Research Council (EPSRC

An Introduction to Social Semantic Web Mining & Big Data Analytics for Political Attitudes and Mentalities Research

The social web has become a major repository of social and behavioral data that is of exceptional interest to the social science and humanities research community. Computer science has only recently developed various technologies and techniques that allow for harvesting, organizing and analyzing such data and provide knowledge and insights into the structure and behavior or people on-line. Some of these techniques include social web mining, conceptual and social network analysis and modeling, tag clouds, topic maps, folksonomies, complex network visualizations, modeling of processes on networks, agent based models of social network emergence, speech recognition, computer vision, natural language processing, opinion mining and sentiment analysis, recommender systems, user profiling and semantic wikis. All of these techniques are briefly introduced, example studies are given and ideas as well as possible directions in the field of political attitudes and mentalities are given. In the end challenges for future studies are discussed

Enriching user profiles using geo-social place semantics in geo-folksonomies

Geo-folksonomies link social web users to geographic places through the tags users choose to label the places with. These tags can be a valuable source of information about the user’s perception of place and can reflect their experiences and activities in the places they label. By analysing the associations between users, places and tags, an understanding of a place and its relationships with other places can be drawn. This place characterisation is unique, dynamic and reflects the perception of a particular user community that generated the geo-folksonomy. In this work, an approach is proposed to analysing geo-folksonomies that builds on and extends existing statistical methods by considering specific concepts of relevance to geographic place resources, namely, place types and place-related activities, and by building a place ontology to encode those concepts and relationships. The folksonomy analysis and evaluation are demonstrated using a realistic geo-folksonomy data set. The resulting ontology is used to build user profiles from the folksonomy. The derived profiles reflect the association between users and the specific places they tag as well as other places with relevant associated place type and activities. The methods proposed here provide the potential for many interesting and useful applications, including the harvesting of useful insight on geographic space and employing the derived user profiles to enhance the search experience and to identify similarities between users based on their association to geographic places