Abstract
In content-based filtering systems, content of items is used to recommend newitems 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. © Springer-Verlag Berlin Heidelberg 2002.
Cite
CITATION STYLE
Polcicovà, G., & Navrat, P. (2002). Semantic similarity in content-based filtering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2435 LNCS, pp. 80–85). Springer Verlag. https://doi.org/10.1007/3-540-45710-0_7
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