You are here

Knowledge-aware and conversational recommender systems

Authors: 

Vito Walter Anelli, Pierpaolo Basile, Derek Bridge, Tommaso Di Noia, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, Markus Zanker

Publication Type: 
Refereed Conference Meeting Proceeding
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.
Conference Name: 
RecSys 2018
Proceedings: 
Proceedings of the 12th ACM Conference on Recommender Systems
Digital Object Identifer (DOI): 
10.1145/3240323.3240338
Publication Date: 
02/10/2018
Pages: 
521-522
Conference Location: 
Canada
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
No