An important characteristic feature of recommender systems for web pages is the abundance of textual information in and about the items being recommended (web pages). To improve recommendations and enhance user experience, we propose to use automatic tag (keyword) extraction for web pages entering the recommender system. We present a novel tag extraction algorithm that employs semi-supervised classification based on a dataset consisting of pre-tagged documents and (for the most part) partially tagged documents whose tags are automatically mined from the content. We also compare several classification algorithms for tag extraction in this context. © 2013 Springer-Verlag.
CITATION STYLE
Leksin, V. A., & Nikolenko, S. I. (2013). Semi-supervised tag extraction in a web recommender system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8199 LNCS, pp. 206–212). https://doi.org/10.1007/978-3-642-41062-8_21
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