Exploiting Social Networks for Recommendation in Online Image Sharing Systems

This thesis aims to demonstrate the distinct and so far little explored value of knowledge derived from social interaction data within large web-scale image sharing systems like Flickr, Picasa Web, Facebook and others for image recommendation. I have shown how such systems can be significantly improved through personalisation that takes into account the social context of users by modelling their interactions by mining data, building and evaluating systems that incorporate this information. These improvements allow users to search and browse large online image collections more quickly and to find results that more accurately match their personal information needs when compared to existing methods. Traditional information retrieval and recommendation datasets are contrived to provide stable baselines for researchers to compare against but they rarely accurately reflect the media systems users tend to encounter online. The online photo sharing site Flickr provides rich and varied data that can be used by researchers to analyse and understand users’ interactions with images and with each other. I analyse such data by modelling the connections between users as multigraphs and exploiting the resultant topologies to produce features that can be used to train recommender systems based on machine learnt classifiers. The core contributions of this work include insight into the nature of very large-scale on- line photo collections and the communities that form around them, as well as the dynamic nature of the interactions users have with their media. I do this through the rigorous evaluation of both a probabilistic tag recommendation system and a machine learnt classifier trained to mimic user decisions regarding image preference. These implementations focus on treating the user as both a unique individual and as a member of potentially many explicit and implicit communities. I also explore the validity of the Flickr ‘Favourite’ feedback label as proxy for user preference, which is particularly important when considering other analogous media systems to which my findings transfer. My conclusions highlight how vital both social context information and the understanding of user behaviour are for online image sharing systems. In the field of information retrieval the diverse nature of users is often forgotten in the hunt for increases in esoteric performance metrics. This thesis places them back at the centre of the problem of multimedia information retrieval and shows how their variety and uniqueness are valuable traits that can be exploited to augment and improve the experience of browsing and searching shared online image collections

Modeling and Understanding Communities in Online Social Media using Probabilistic Methods

The amount of multimedia content is on a constant increase, and people interact with each other and with content on a daily basis through social media systems. The goal of this thesis was to model and understand emerging online communities that revolve around multimedia content, more specifically photos, by using large-scale data and probabilistic models in a quantitative approach. The dissertation has four contributions. First, using data from two online photo management systems, this thesis examined different aspects of the behavior of users of these systems pertaining to the uploading and sharing of photos with other users and online groups. Second, probabilistic topic models were used to model online entities, such as users and groups of users, and the new proposed representations were shown to be useful for further understanding such entities, as well as to have practical applications in search and recommendation scenarios. Third, by jointly modeling users from two different social photo systems, it was shown that differences at the level of vocabulary exist, and different sharing behaviors can be observed. Finally, by modeling online user groups as entities in a topic-based model, hyper-communities were discovered in an automatic fashion based on various topic-based representations. These hyper-communities were shown, both through an objective and a subjective evaluation with a number of users, to be generally homogeneous, and therefore likely to constitute a viable exploration technique for online communities

Introduction: Ways of Machine Seeing

How do machines, and, in particular, computational technologies, change the way we see the world? This special issue brings together researchers from a wide range of disciplines to explore the entanglement of machines and their ways of seeing from new critical perspectives. This 'editorial' is for a special issue of AI & Society, which includes contributions from: María Jesús Schultz Abarca, Peter Bell, Tobias Blanke, Benjamin Bratton, Claudio Celis Bueno, Kate Crawford, Iain Emsley, Abelardo Gil-Fournier, Daniel Chávez Heras, Vladan Joler, Nicolas Malevé, Lev Manovich, Nicholas Mirzoeff, Perle Møhl, Bruno Moreschi, Fabian Offert, Trevor Paglan, Jussi Parikka, Luciana Parisi, Matteo Pasquinelli, Gabriel Pereira, Carloalberto Treccani, Rebecca Uliasz, and Manuel van der Veen

McNair Scholars Research Journal Volume V

https://commons.stmarytx.edu/msrj/1004/thumbnail.jp

McNair Scholars Research Journal Volume V

https://commons.stmarytx.edu/msrj/1004/thumbnail.jp

Located Lexicon: a project that explores how user generated content describes place

This extended conference paper explores the use and potential of location data in social media contexts. The research involved a series of experiments undertaken to assess the extent to which location information is present in exchanges, directly or indirectly. A prototype application was designed to exploit the insight obtained from the data-gathering experiments. This enabled us to develop a method and toolkit for searching, extracting and visualising mass-generated data for open source use. Ultimately, we were able to generate insights into data quality and ‘scale of query’ for emerging pedagogical research in learning swarms and distributed learners

Data-driven approaches for interactive appearance editing

This thesis proposes several techniques for interactive editing of digital content and fast rendering of virtual 3D scenes. Editing of digital content - such as images or 3D scenes - is difficult, requires artistic talent and technical expertise. To alleviate these difficulties, we exploit data-driven approaches that use the easily accessible Internet data (e. g., images, videos, materials) to develop new tools for digital content manipulation. Our proposed techniques allow casual users to achieve high-quality editing by interactively exploring the manipulations without the need to understand the underlying physical models of appearance. First, the thesis presents a fast algorithm for realistic image synthesis of virtual 3D scenes. This serves as the core framework for a new method that allows artists to fine tune the appearance of a rendered 3D scene. Here, artists directly paint the final appearance and the system automatically solves for the material parameters that best match the desired look. Along this line, an example-based material assignment approach is proposed, where the 3D models of a virtual scene can be "materialized" simply by giving a guidance source (image/video). Next, the thesis proposes shape and color subspaces of an object that are learned from a collection of exemplar images. These subspaces can be used to constrain image manipulations to valid shapes and colors, or provide suggestions for manipulations. Finally, data-driven color manifolds which contain colors of a specific context are proposed. Such color manifolds can be used to improve color picking performance, color stylization, compression or white balancing.Diese Dissertation stellt Techniken zum interaktiven Editieren von digitalen Inhalten und zum schnellen Rendering von virtuellen 3D Szenen vor. Digitales Editieren - seien es Bilder oder dreidimensionale Szenen - ist kompliziert, benötigt künstlerisches Talent und technische Expertise. Um diese Schwierigkeiten zu relativieren, nutzen wir datengesteuerte Ansätze, die einfach zugängliche Internetdaten, wie Bilder, Videos und Materialeigenschaften, nutzen um neue Werkzeuge zur Manipulation von digitalen Inhalten zu entwickeln. Die von uns vorgestellten Techniken erlauben Gelegenheitsnutzern das Editieren in hoher Qualität, indem Manipulationsmöglichkeiten interaktiv exploriert werden können ohne die zugrundeliegenden physikalischen Modelle der Bildentstehung verstehen zu müssen. Zunächst stellen wir einen effizienten Algorithmus zur realistischen Bildsynthese von virtuellen 3D Szenen vor. Dieser dient als Kerngerüst einer Methode, die Nutzern die Feinabstimmung des finalen Aussehens einer gerenderten dreidimensionalen Szene erlaubt. Hierbei malt der Künstler direkt das beabsichtigte Aussehen und das System errechnet automatisch die zugrundeliegenden Materialeigenschaften, die den beabsichtigten Eigenschaften am nahesten kommen. Zu diesem Zweck wird ein auf Beispielen basierender Materialzuordnungsansatz vorgestellt, für den das 3D Model einer virtuellen Szene durch das simple Anführen einer Leitquelle (Bild, Video) in Materialien aufgeteilt werden kann. Als Nächstes schlagen wir Form- und Farbunterräume von Objektklassen vor, die aus einer Sammlung von Beispielbildern gelernt werden. Diese Unterräume können genutzt werden um Bildmanipulationen auf valide Formen und Farben einzuschränken oder Manipulationsvorschläge zu liefern. Schließlich werden datenbasierte Farbmannigfaltigkeiten vorgestellt, die Farben eines spezifischen Kontexts enthalten. Diese Mannigfaltigkeiten ermöglichen eine Leistungssteigerung bei Farbauswahl, Farbstilisierung, Komprimierung und Weißabgleich

The cause, development and outcome of word-of-mouth marketing: with particular reference to WOM volume, valence and the modeling of viral marketing

Viral marketing is a form of online word-of-mouth (WOM) communication in which individuals are encouraged to pass on promotional messages through social websites. With the growing popularity of online social websites, viral marketing has increasingly garnered attention of marketers and marketing researchers alike. The two most important WOM attributes highlighted in the extant literature are volume and valence. This thesis looked into the cause, development and outcome of WOM marketing and provided computational models for forecasting the development of WOM volume and valence of viral marketing in social websites. With the data extracted from large-scale web-crawling activities, through a series of computer simulation experiments comparable to social websites, the author developed models to predict WOM volume and valence in viral marketing. The model for predicting WOM volume in viral marketing used theories of network topologies. The model for predicting WOM valence in viral marketing used an artificial neural network model. The author discussed the insights from the findings and suggested viral marketing strategies to optimize the performance of WOM volume and valence in social websites. A key contribution of this thesis is the new approaches of modeling and data collection for WOM volume and valance forecasting in viral marketing