@inbook{036cdbb7491e4f8b9202069f82cc80a3,
title = "Knowledge-aware and conversational recommender systems",
abstract = "More and more precise and powerful recommendation algorithms and techniques have been proposed over the last years able to effectively assess users' tastes and predict information that would probably be of interest for them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take into account the huge amount of knowledge, both structured and non-structured ones, describing the domain of interest for the recommendation engine. The aim of knowledge-aware and conversational recommender systems is to go beyond the traditional accuracy goal and to start a new generation of algorithms and interactive approaches which exploit the knowledge encoded in ontological and logic-based knowledge bases, knowledge graphs as well as the semantics emerging from the analysis and exploitation of semi-structured textual sources.",
keywords = "Conversational agents, Knowledge base, Knowledge graph, Knowledge-aware, Linked data, Natural language processing",
author = "Anelli, \{Vito Walter\} and Noia, \{Tommaso Di\} and Pierpaolo Basile and Pasquale Lops and Derek Bridge and Cataldo Musto and Fedelucio Narducci and Markus Zanker",
note = "Publisher Copyright: {\textcopyright} 2018 Copyright held by the owner/author(s).; 12th ACM Conference on Recommender Systems, RecSys 2018 ; Conference date: 02-10-2018 Through 07-10-2018",
year = "2018",
month = sep,
day = "27",
doi = "10.1145/3240323.3240338",
language = "English",
series = "RecSys 2018 - 12th ACM Conference on Recommender Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "521--522",
booktitle = "RecSys 2018 - 12th ACM Conference on Recommender Systems",
}