This paper proposes a movie genreprediction based on multinomial probability model. To the best of our knowledge, this problem has not been addressed yet in the field of recommender system. The prediction of a movies genre has many practical applications including complementing the items categories given by experts and providing a surprise effect in the recommendations given to a user. We emplo...

This paper presents some findings around musical genres. The main goal is to analyse whether there is any agreement between a group of experts and a community, when defining a set of genres and their relationships. For this purpose, three different experiments are conducted using two datasets: the MP3.com expert taxonomy, and last.fm tags at artist level. The experimental results show a clear a...

this study aims at uncovering the effect(s) of genre-based instruction (gbi) on listening proficiency among iranian efl learners. moreover, it seeks to explore the relationship between effectiveness of gbi and listening proficiency. for this purpose, 68 efl learners in two different groups at zabol university were selected. group a included 30 participants (12 males and 18 females), and group b...

In this paper, we propose an audio-visual approach to video genre categorization. It exploits audio, color, temporal and contour information, which are in general genre specific. Audio information is extracted at block-level, which has the advantage of capturing local temporal information. At temporal level, we asses action contents with respect to human perception. Further, color perception is...

This paper reports on an approach to the analysis of form (layout and formatting) during genre recognition recorded using eye tracking. The researchers focused on eight different types of e-mail, such as calls for papers, newsletters and spam, which were chosen to represent different genres. The study involved the collection of oculographic behaviour data based on the scanpath duration and scan...

Recently, feature representation by learning algorithms has drawn great attention. In the music domain, it is either unsupervised or supervised by semantic labels such as music genre. However, finding discriminative features in an unsupervised way is challenging, and supervised feature learning using semantic labels may involve noisy or expensive annotation. In this paper, we present a feature ...

This dissertation presents, discusses, and sheds some light on the problems that appear when computers try to automatically classify musical genres from audio signals. In particular, a method is proposed for the automatic music genre classification by using a computational approach that is inspired in music cognition and musicology in addition to Music Information Retrieval techniques. In this ...

This paper presents a novel approach for post-processing the music genre hypotheses generated by a baseline classifier. Given a music piece, the baseline classifier produces a ranked list of the N best hypotheses consisting of music genre labels and recognition scores. A rejection strategy is then applied to either reject or accept the output of the baseline classifier. Some of the rejected ins...

Manifold approaches exist for organization of music by genre and/or style. In this paper we propose the use of text categorization techniques to classify artists present on the Internet. In particular, we retrieve and analyze webpages ranked by search engines to describe artists in terms of word occurrences on related pages. To classify artists we primarily use support vector machines. We prese...