Abstract
In content-based filtering systems, content of items is used to recommend newi tems to the users. It is usually represented by words in natural language where meanings of words are often ambiguous. We studied clustering of words based on their semantic similarity. Then we used word clusters to represent items for recommending new items by content-based filtering. In the paper we present our empirical results.
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Polčicová, G., Návrat, P. (2002). Semantic Similarity in Content-Based Filtering. In: Manolopoulos, Y., Návrat, P. (eds) Advances in Databases and Information Systems. ADBIS 2002. Lecture Notes in Computer Science, vol 2435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45710-0_7
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DOI: https://doi.org/10.1007/3-540-45710-0_7
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