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...

In this paper, we shall introduce a system for extracting the keyphrases for the reason of authors’ opinion from product reviews. The datasets for two fairly different product review domains related to movies and mobile phones were constructed semiautomatically based on the pros and cons entered by the authors. The system illustrates that the classic supervised keyphrase extraction approach – m...

Many research domains use video contents as stimuli for study on human emotions.A video content within a particular genre or a specific the meevokes dynamic emotions that are highly useful in many research fields. The present study proposed a set of video-clip stimuli that embody four emotions under specific genres of movies, namely happiness, calmness, sadness and fear. Two experiments (a prel...

This paper details our analysis of the Internet Movie Database (IMDb), a free, user-maintained, online resource of production details for over 390,000 movies, television series and video games, which contains information such as title, genre, box-office taking, cast credits and user's ratings. We gather a series of interesting facts and relationships using a variety of data mining techniques. I...

BACKGROUND This study examines the portrayals of soft drinks in popular American movies as a potential vehicle for global marketing and an indicator of covert product placement. METHODS We conducted a content analysis of America's top-ten grossing films from 1991 through 2000 that included portrayals of beverages (95 movies total). Coding reliabilities were assessed with Cohen's kappa, and ex...

Our project intends to classify movies into the three most probable genres that they belong to, from a predefined set of 25 genres, based on only one image i.e movie poster. We have made use Convolutional Neural Networks (CNN) realize this as we believe it would be help extract features and visual information image. Instead multi-class classification problem in which input is classified any cla...