Abstract
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.
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Kunaver, M., Požrl, T., Dobravec, Š., Droftina, U., Košir, A. (2014). Increasing Top-20 Diversity Through Recommendation Post-processing. In: Presutti, V., et al. Semantic Web Evaluation Challenge. SemWebEval 2014. Communications in Computer and Information Science, vol 475. Springer, Cham. https://doi.org/10.1007/978-3-319-12024-9_25
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DOI: https://doi.org/10.1007/978-3-319-12024-9_25
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