Abstract : Let's imagine a system that can recommend the kind of mu-sic (among other application domains) you like to listen when you are at work, without having to know your location, IP address or even to ask your current mood. In this paper, we bring this dream closer by proposing a model that can automatically understand the user's current context. This model, called DANCE, analyzes the attributes of the items in your recent history and monitors the relative diversity brought by your consultations over time. We validated our approach with a music corpus of 100 users and a global history of 204,758 plays.
https://hal.inria.fr/hal-01108990 Contributor : Sylvain CastagnosConnect in order to contact the contributor Submitted on : Friday, January 23, 2015 - 6:27:12 PM Last modification on : Saturday, October 16, 2021 - 11:26:08 AM Long-term archiving on: : Friday, April 24, 2015 - 10:55:43 AM
Amaury L'Huillier, Sylvain Castagnos, Anne Boyer. Understanding Usages by Modeling Diversity over Time. 22nd Conference on User Modeling, Adaptation, and Personalization, Aalborg University, Denmark, Jul 2014, Aalborg, Denmark. ⟨hal-01108990⟩