Increasing Top-20 Diversity Through Recommendation Post-processing

This paper presents two different methods for diversifying recommendations that were developed as part of the ESWC2014 challenge. Both methods focus on post-processing recommendations provided by the baseline recommender system and have increased the ILD at the cost of final precision (measured with...

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Bibliographic Details
Published inSemantic Web Evaluation Challenge pp. 188 - 192
Main Authors Kunaver, Matevž, Požrl, Tomaž, Dobravec, Štefan, Droftina, Uroš, Košir, Andrej
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesCommunications in Computer and Information Science
Subjects
Diversity
ILD
Recommender system
RecSys
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Summary:This paper presents two different methods for diversifying recommendations that were developed as part of the ESWC2014 challenge. Both methods focus on post-processing recommendations provided by the baseline recommender system and have increased the ILD at the cost of final precision (measured with F@20). The authors feel that this method has potential yet requires further development and testing.
ISBN:3319120239
9783319120232
ISSN:1865-0929
1865-0937
DOI:10.1007/978-3-319-12024-9_25