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.
|Titolo:||Linked Open Data-Enabled Recommender Systems: ESWC 2014 Challenge on Book Recommendation|
|Titolo del libro:||Semantic Web Evaluation Challenge : SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers|
|Data di pubblicazione:||2014|
|Digital Object Identifier (DOI):||10.1007/978-3-319-12024-9_17|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|