@inbook{56d925cd0ab24331a2e95345775f4020,
title = "Recommending from experience",
abstract = "In this paper we present RC, a context-driven recommender system that mines contextual information from usergenerated reviews and makes recommendations based on the users' experiences. RC mines the contextual information from the user-generated reviews using a form of topic modeling. This means that, unlike other context-aware recommender systems, RC does not have a predefined set of contextual variables. After mining the contextual information, RC makes top-n recommendations using a Factorization Machine with the contextual topics as side information. Our experiments on two datasets of ratings and reviews show that RC has higher recall than a conventional recommender.",
author = "Pe{\~n}a, \{Francisco J.\} and Derek Bridge",
note = "Publisher Copyright: Copyright {\textcopyright} 2017, Association for the Advancement of Artificial Intelligence.; 30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017 ; Conference date: 22-05-2017 Through 24-05-2017",
year = "2017",
language = "English",
series = "FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference",
publisher = "AAAI Press",
pages = "651--656",
editor = "Vasile Rus and Zdravko Markov",
booktitle = "FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference",
}