In this chapter we present a report of the ESWC 2014 Challenge on Linked Open Data-enabled Recommender Systems, which consisted of three tasks in the context of book recommendation: rating prediction in cold-start situations, top N recommendations from binary user feedback, and diversity in content-based recommendations. Participants were requested to address the tasks by means of recommendation approaches that made use of Linked Open Data and semantic technologies. In the chapter we describe the challenge motivation, goals and tasks, summarize and compare the nine final participant recommendation approaches, and discuss their experimental results and lessons learned. Finally, we end with some conclusions and potential lines of future research.
Linked Open Data-Enabled Recommender Systems: ESWC 2014 Challenge on Book Recommendation / Di Noia, Tommaso; Cantador, Iván; Ostuni, Vito Claudio (COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE). - In: Semantic Web Evaluation Challenge : SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers / [a cura di] Valentina Presutti, Milan Stankovic, Erik Cambria, Ivàn Cantador, Angelo Di Iorio, Tommaso Di Noia, Christoph Lange, Diego Reforgiato Recupero, Anna Tordai. - STAMPA. - Cham, CH : Springer, 2014. - ISBN 978-3-319-12023-2. - pp. 129-143 [10.1007/978-3-319-12024-9_17]
Linked Open Data-Enabled Recommender Systems: ESWC 2014 Challenge on Book Recommendation
Tommaso Di Noia;Vito Claudio Ostuni
2014-01-01
Abstract
In this chapter we present a report of the ESWC 2014 Challenge on Linked Open Data-enabled Recommender Systems, which consisted of three tasks in the context of book recommendation: rating prediction in cold-start situations, top N recommendations from binary user feedback, and diversity in content-based recommendations. Participants were requested to address the tasks by means of recommendation approaches that made use of Linked Open Data and semantic technologies. In the chapter we describe the challenge motivation, goals and tasks, summarize and compare the nine final participant recommendation approaches, and discuss their experimental results and lessons learned. Finally, we end with some conclusions and potential lines of future research.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
