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.

Knowledge-aware and conversational recommender systems / Anelli, Vito Walter; Basile, Pierpaolo; Bridge, Derek; Di Noia, Tommaso; Lops, Pasquale; Musto, Cataldo; Narducci, Fedelucio; Zanker, Markus. - STAMPA. - (2018), pp. 521-522. (Intervento presentato al convegno 12th ACM Conference on Recommender Systems, RECSYS 2018 tenutosi a Vancouver, Canada nel October 2-7, 2018) [10.1145/3240323.3240338].

Knowledge-aware and conversational recommender systems

Vito Walter Anelli;Tommaso Di Noia;Fedelucio Narducci;
2018-01-01

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.
2018
12th ACM Conference on Recommender Systems, RECSYS 2018
978-1-4503-5901-6
Knowledge-aware and conversational recommender systems / Anelli, Vito Walter; Basile, Pierpaolo; Bridge, Derek; Di Noia, Tommaso; Lops, Pasquale; Musto, Cataldo; Narducci, Fedelucio; Zanker, Markus. - STAMPA. - (2018), pp. 521-522. (Intervento presentato al convegno 12th ACM Conference on Recommender Systems, RECSYS 2018 tenutosi a Vancouver, Canada nel October 2-7, 2018) [10.1145/3240323.3240338].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/215898
Citazioni
  • Scopus 20
  • ???jsp.display-item.citation.isi??? 12
social impact