This paper presents a framework for the classification of feature films into genres, based on computable visual cues. The authors view the work as a step towards high-level semantic film interpretation, currently using low-level video features and knowledge of ubiquitous cinematic practices. Our current domain of study is the film preview (the commercial advertisements primarily created to attr...

Statistical classifiers operate on features that generally include both useful and useless information. These two types of information are difficult to separate in the feature domain. Recently, a new paradigm based on a Latent Factor Analysis (LFA) proposed a model decomposition into usefull and useless components. This method was successfully applied to speaker and language recognition tasks. ...

With the exponential increase in the integration of mobile communication devices with media communication over the Internet, the need to transmit high quality archived media clips and movies over limited bandwidth connections has increased. Archived movies have immense use for entertainment such as cartoon movies, news clips, and Internet based mobile instructions. In this paper, we describe an...

A variety of theoretical foundations and methodologies populate the IS field of research. At odds, there is a general uniformity in the ways of presenting the outcome of research. As a result, traditional empirical research structures and wording may inadequately conveys research results to the relevant audience or undermine the potential impact of IS studies. To overcome such limitations, alte...

Skipforward is a distributed annotation system allowing users to enter and browse statements about items and their features. Items can be things such as movies or books; item features are the genre of a movie or the storytelling pace of a book. Whenever multiple users annotate the same item with a statement about the same feature, these individual statements get aggregated by the system. For ag...

Online content and service providers deal with the problem of providing “relevant” content on a regular basis, especially due to the sheer volume of data available. This work deals with one such problem, namely, that of predicting user preference for movies using the NetFlix database. We present a memory-based Collaborative Filtering (CF) algorithm that learns the personality traits of the user...

Video games are part of our culture like TV, movies, and books. We believe that this kind of software can be used to increase students’ interest in computer science. Video games with other goals than entertainment, serious games, are present, today, in several fields such as education, government, health, defence, industry, civil security, and science. This paper presents a study around a serio...

FALL 2015 73 The recent explosion of content (such as movies, TV shows, sports, and others) available on television coupled with an increase use in mobile devices (that is, smartphones and tablets) has created significant interest in second-screen applications from both end users and content providers. Second screen applications are designed to run from mobile devices and to enhance the televis...

Collaborative filtering recommends items to a user based on the interests of other users having similar preferences. However, high dimensional, sparse data result in poor performance in collaborative filtering. This paper introduces an approach called multiple metadata-based collaborative filtering (MMCF), which utilizes meta-level information to alleviate this problem, e.g., metadata such as g...

The aim of this study was to investigate the use and potential of the psychological theory of human-behavior modeling, called the Theory of Planned Behavior (TPB), in a user-modeling domain. We performed a user experiment involving a well-studied problem of user modeling, i.e., a recommender system (RS) for movies. As a part of the TPB, a survey to estimate the behavioral, normative and control...