Publications de Florent Frédéric Garcin

Recommender system for an online multimedia content provider

A recommender system for determining multimedia items to be presented in an online interface of a multimedia content provider. The recommender system continuously observes the user's behavior in order to define a context for the user which on the basis of ...

2016

Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics

Boi Faltings, Florent Frédéric Garcin, Andrii Maksai

We investigate how metrics that can be measured offline can be used to predict the online performance of recommender systems, thus avoiding costly A-B testing. In addition to accuracy metrics, we combine diversity, coverage, and serendipity metrics to crea ...

2015

Aggregating information from the crowd : ratings, recommendations and predictions

Florent Frédéric Garcin

With an ever-growing amount of data generated on the web, aggregating information from the crowd into meaningful knowledge has become crucial to companies in order to create a competitive edge. Not only companies, but also organizations and governments can ...

EPFL2014

PEN recsys: a Personalized News Recommender Systems Framework (DEMO)

Boi Faltings, Florent Frédéric Garcin

We present the Personalized News (PEN) recommender systems framework, (Note that this paper is an extended abstract of Garcin2013b), currently in use by a newspaper website to evaluate various algorithms for news recommendations. We briefly describe its sy ...

2013

Personalized News Recommendation with Context Trees

Boi Faltings, Christos Dimitrakakis, Florent Frédéric Garcin

The proliferation of online news creates a need for filtering interesting articles. Compared to other products, however, recommending news has specific challenges: news preferences are subject to trends, users do not want to see multiple articles with simi ...

2013

PEN recsys: a Personalized News Recommender Systems Framework

Boi Faltings, Florent Frédéric Garcin

We present the Personalized News (PEN) recommender systems framework, currently in use by a newspaper website to evaluate various algorithms for news recommendations. We briefly describe its system architecture and related components. We show how a researc ...

2013

Personalized news recommendation based on collaborative filtering

Boi Faltings, Vincent Jean Fabrice Schickel, Florent Frédéric Garcin

Because of the abundance of news on the web, news recommendation is an important problem. We compare three approaches for personalized news recommendation: collaborative filtering at the level of news items, content-based system recommending items with sim ...

2012

Reporting incentives and biases in online review forums

Boi Faltings, Radu Jurca, Florent Frédéric Garcin

Online reviews have become increasingly popular as a way to judge the quality of various products and services. However, recent work demonstrates that the absence of reporting incentives leads to a biased set of reviews that may not reflect the true qualit ...

2010

Rating aggregation in collaborative filtering systems

Boi Faltings, Radu Jurca, Florent Frédéric Garcin, Nadine Joswig

Recommender systems based on user feedback rank items by aggregating users' ratings in order to select those that are ranked highest. Ratings are usually aggregated using a weighted arithmetic mean. However, the mean is quite sensitive to outliers and bias ...

ACM Press2009

Aggregating Reputation Feedback

Boi Faltings, Radu Jurca, Florent Frédéric Garcin

A fundamental task in reputation systems is to aggregate multiple feedback ratings into a single value that can be used to compare the reputation of different entities. Feedback is most commonly aggregated using the arithmetic mean. However, the mean is qu ...

2009