IMDb is a widely known online movie platform that offers movie information, allows to rate movies and recommends interesting movies to users. The IMDb movie recommendations do not however offer any means for interactivity or user control, which inherently limits their contextual adaptability. In this work we describe our Google Chrome extension – called MovieBrain – which offers interactive mov...

Piracy of trademarked, copyrighted, and/or branded goods and services is a critical factor in many industries. In the movie industry, which we study here, the Motion Picture Association of America (MPAA) estimates that piracy costs US movie studios $6.1 billion in 2005, the latest year for which data are available. (www.mpaa.org) The impact of piracy is viewed as so important to the movie indus...

The fear of speaking is often cited as the world's most common social phobia. The rapid growth of computer technology enabled us to use virtual reality (VR) for the treatment of the fear of public speaking. There have been two techniques used to construct a virtual environment for the treatment of the fear of public speaking: model-based and movie-based. Virtual audiences and virtual environmen...

This paper looks into a new direction in movie clips analysis –model based ranking of highlight level. A movie clip, containing a short story, is composed of several continuous shots, which is much simpler than the whole movie. As a result, clip based analysis provides a feasible way for movie analysis and interpretation. In this paper, clip-based ranking of highlight level is proposed, where t...

This paper gives a description of the empirical study investigating the impact of promotions using Web based tools by Indian Movie production houses. Evolution of movie promotion starting with use of traditional methods for the first movie to the near dependence on social media to promote the latest movies produced in India has been chronologically presented. Critical role of web based tools an...

KDD Cup 2007 focuses on predicting aspects of movie rating behavior. We present our prediction method for Task 1 “Who Rated What in 2006” where the task is to predict which users rated which movies in 2006. We use the combination of the following predictors, listed in the order of their efficiency in the prediction: • The predicted number of ratings for each movie based on time series predictio...

The information on the web is not only published by an original language, but also expressed in many different languages. Almost recommendation systems also lack mechanisms to support users overcoming the language problem. In these systems, it is difficult to search a specific value (e.g., movie artist, movie title in movie domain) by using native language. In this paper, we present our approac...

Digital movie systems o er great perspectives for multimedia applications. But the large amounts of data involved and the demand for isochronous transmission and playback are also great challenges for the designers of a new generation of le systems, database systems, operating systems, window systems, video encoder/decoders and networks. Today's research prototypes of digital movie systems su e...

I ndia produces the largest number of movies in the world, and the country is also home to one of the largest number of people born blind [1]. Yet it is only now that the 90-year-old Hindi film industry, based in Mumbai, has made a movie based on the life of Helen Keller, arguably the most inspirational visually challenged person in history. Because films are supposed to be a means of transient...

Cross-modal data retrieval has been the basis of various creative tasks performed by Arti€cial Intelligence (AI). One such highly challenging task for AI is to convert a book into its corresponding movie, which most of the creative €lm makers do as of today. In this research, we take the €rst step towards it by visualizing the content of a book using its corresponding movie visuals. Given a set...