Publications de Vincent Jean Fabrice Schickel

Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin

We describe the selection, implementation and online evaluation of two e-commerce recommender systems developed with our partner company, Prediggo. The first one is based on the novel method of Bayesian Variable-order Markov Modeling (BVMM). The second, SS ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2019

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

Methods of inferring user preferences using ontologies

Boi Faltings, Vincent Jean Fabrice Schickel

Methods of inferring user preferences using ontologies use ontology structure based similarity and an inference mechanism in defining ontology filtering (OF). Items are recommended to users by identifying items that the users may prefer, but may not have b ...

2008

Using Hierarchical Clustering for Learning the Ontologies used in Recommendation Systems

Boi Faltings, Vincent Jean Fabrice Schickel

Ontologies are being successfully used to overcome semantic heterogeneity, and are becoming fundamental elements of the Semantic Web. Recently, it has also been shown that ontologies can be used to build more accurate and more personalized recommendation s ...

2007

Ontology filtering

Vincent Jean Fabrice Schickel

With the ever growing importance of internet, people are becoming overwhelmed by information. More concretely, consider a situation where you find yourself with an evening alone and would like to rent a DVD to watch. For several reasons, this is a difficul ...

EPFL2007

OSS: A semantic Similarity Function based on Hierarchical Onotlogies

Boi Faltings, Vincent Jean Fabrice Schickel

2007

Overcoming Incomplete User Models in Recommendation Systems Via an Ontology

Boi Faltings, Vincent Jean Fabrice Schickel

To make accurate recommendations, recommendation systems currently require more data about a customer than is usually available. We conjecture that the weaknesses are due to a lack of inductive bias in the learning methods used to build the prediction mode ...

Springer2006

Vincent Jean Fabrice Schickel

Graph Chatbot

Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

Personalized news recommendation based on collaborative filtering

Methods of inferring user preferences using ontologies

Using Hierarchical Clustering for Learning the Ontologies used in Recommendation Systems

Ontology filtering

OSS: A semantic Similarity Function based on Hierarchical Onotlogies

Overcoming Incomplete User Models in Recommendation Systems Via an Ontology

Using an Ontological A-priori Score To Infer User's Preferences

Inferring User's Preferences using Ontologies

Stimulating Preference Expression Using Suggestions

Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

Personalized news recommendation based on collaborative filtering

Methods of inferring user preferences using ontologies

Using Hierarchical Clustering for Learning the Ontologies used in Recommendation Systems

OSS: A semantic Similarity Function based on Hierarchical Onotlogies

Inferring User's Preferences using Ontologies

Overcoming Incomplete User Models in Recommendation Systems Via an Ontology

Ontology filtering

Stimulating Preference Expression Using Suggestions

Using an Ontological A-priori Score To Infer User's Preferences