Abstract These days, a recommendation of movie from server-based system has made finding piece cinema easier. Film helps us to find films that we need watch, instead searching extensively online and help cinephiles buffs by suggesting top tier watch without looking into huge databases which is very time consuming. As an approach this dilemma, Introduce model based on collaborative content-based...

Recommender systems have become prevalent in recent years as they help users to access relevant items from the vast universe of possibilities available these days. Most existing research in this area is based purely on quantitative aspects such as indices of popularity or measures of similarity between items or users. This work introduces a novel perspective on movie recommendation that combine...

Abstract This chat presents a novel approach to automated product recommendation that uses the popularity characteristics of products. Although they play a significant role in the consumer purchasing process, there has been little attention paid to the use of popularity in recommendation research so far. In order to use popularity features, this paper develops a threedimensional model of popula...

The aim of this project is to learn how to make predictions on what genre of movie a user is likely to be interested in based on their raw Facebook data. We collaborated with other groups to collect a dataset of 10k unique users which was then parsed, stemmed and tokenized. We trained two predictors each using a di erent model, SVM and Naive Bayes. In order to correct for the skew in the traini...

Typically a user prefers an item (e.g., a movie) because she likes certain features of the item (e.g., director, genre, producer). This observation motivates us to consider a featurecentric recommendation approach to item recommendation: instead of directly predicting the rating on items, we predict the rating on the features of items, and use such ratings to derive the rating on an item. This ...

We compare classic text classification techniques with more recent machine learning techniques and introduce a novel architecture that outperforms many state-of-the-art approaches. These techniques are evaluated on a new multi-label classification task, where the task is to predict the genre of a movie based on its subtitle. We show that pre-trained word embeddings contain ’universal’ features ...

Research in statistical machine translation (SMT) is largely driven by formal translation tasks, while translating informal text is much more challenging. In this paper we focus on SMT for the informal genre of dialogues, which has rarely been addressed to date. Concretely, we investigate the effect of dialogue acts, speakers, gender, and text register on SMT quality when translating fictional ...

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...

Social robots can be used as interfaces to provide recommendations to users. While a vast literature compares the user’s behavior when interacting with a robot with respect to a virtual agent, in this paper, we conduct a first evaluation on how the user’s choices are affected if the recommendations are provided respectively by a mobile application or by social robots with different degree of in...