Preference-based recommendation systems have transformed how we consume media. By analyzing usage data, these methods uncover our latent preferences for items (such as articles or...
Allison June-Barlow Chaney, David M. Blei, Tina El...
The objective of this PhD research is to deepen the understanding of how people listen to music and construct playlists. We believe that further insights into such mechanisms can ...
Web augmentation techniques allow the adaptation of websites on client side using browser extensions or plug-ins designed to run dedicated user scripts. However, while number and ...
Martin Wischenbart, Sergio Firmenich, Gustavo Ross...
In this paper, we propose new metrics to accurately measure the concentration reinforcement of recommender systems and the enhancement of the “long tail”. We also conduct a co...
Panagiotis Adamopoulos, Alexander Tuzhilin, Peter ...
In this paper, we present work-in-progress on a recommender system based on Collaborative Filtering that exploits location information gathered by indoor positioning systems. This...
Traditional approaches for music recommender systems face the known challenges of providing new recommendations that users perceive as novel and serendipitous discoveries. Even wi...
Complex heterogeneous networks contain many types of relations, both local to a particular entity and distant in the network. Multi-relational factorization schemes that incorpora...
Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher
We describe an architecture for generating context-aware recommendations along with detailed textual explanations to support the user in the decision-making process. CARE (Context...
This paper describes some of the key properties of the proposed solution for the RecSys 2015 Challenge from the team Tøyvind thørrud. Three contributions will be highlighted: i)...
Over the last years, thanks to Open Data initiative and the Semantic Web, there has been a vast increase on user contributed data. In several cases (e.g. OpenStreetMap, Geonames),...