Abstract
Content-based recommender systems can overcome many problems related to collaborative filtering systems, such as the new-item issue. However, to make accurate recommendations, content-based recommenders require an adequate amount of content, and external knowledge sources are used to augment the content. In this paper, we use Wordnet synsets to enrich a content-based joke recommender system. Experiments have shown that content-based recommenders using K-nearest neighbors perform better than collaborative filtering, particularly when synsets are used.
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Alharthi, H., Inkpen, D. (2015). Content-Based Recommender System Enriched with Wordnet Synsets. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_22
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DOI: https://doi.org/10.1007/978-3-319-18117-2_22
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