The present study investigated the effect of single and multiple genre movies on listening comprehension of upper intermediate language learners. To this end, a language proficiency test was administered to 40 male and female postgraduates of different majors attending International English Language Testing System (IELTS) classes and ultimately 25 upper intermediate language learners were selec...

Movie ratings play an important role both in determining the likelihood of a potential viewer to watch the movie and in reflecting the current viewer satisfaction with the movie. They are available in several sources like the television guide, best-selling reference books, newspaper columns, and television programs. Furthermore, movie ratings are crucial for recommendation engines that track th...

the present study investigated the effect of single and multiple genre movies on listening comprehension of upper intermediate language learners. to this end, a language proficiency test was administered to 40 male and female postgraduates of different majors attending international english language testing system (ielts) classes and ultimately 25 upper intermediate language learners were selec...

It is often the case that a human can determine the genre of a movie by looking at its movie poster. This task is not trivial for computers. A recent advance in machine learning called deep learning allows algorithms to learn important features from large datasets. Rather than analyzing an image pixel by pixel, for example, higher level features can be used for classification. In this project I...

We present a method of automatically constructing a domain-specific Korean sentiment dictionary which can be used to classify the sentiment of online movie reviews. More than 1.18 million online movie reviews with movie ratings ranging between 1 to 4 and 7 to 10 were collected across fourteen different movie genres to calculate the joint probability of a given word and the sentiment of movie re...

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 proposes a system that automatically locates and extracts songs from digitized movies and classifies the extracted songs according to their genre using a video-based genre identification algorithm. A song grammar particularly applicable to Bollywood movies has been proposed and used subsequently to construct a probabilistic timed automaton that differentiates songs from rest of the s...

Making decision on which movie to watch is nowadays not an easy process for majority of people. Many people may decide to watch a movie based on the genre attribute of a movie, while for the others, the director can be the attribute that drives them to watch a movie. Hence, people may have di↵erent features, taken into account, when deciding which movie to watch. Recommender Systems can help pe...

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...

Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on traditional features (attributes) such as tag, genre, and cast. Typically, movie features are human-generated, either editorially (e.g., genre and cast) or by leveraging the wisdom of the crowd (e.g., tag), and as such, they are prone to...