Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metrik

Dominik Kowald, Emanuel Lacic, Dieter Theiler, Matthias Traub, Lucky Kuffer , Stefanie Lindstaedt, Elisabeth Lex

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


In this paper, we present our work to support publishers and editors in finding descriptive tags for e-books through tag recommendations. We propose a hybrid tag recommendation system for e-books, which leverages search query terms from Amazon users and e-book metadata, which is assigned by publishers and editors. Our idea is to mimic the vocabulary of users in Amazon, who search for and review e-books, and to combine these search terms with editor tags in a hybrid tag recommendation approach. In total, we evaluate 19 tag recommendation algorithms on the review content of Amazon users, which reflects the readers' vocabulary. Our results show that we can improve the performance of tag recommender systems for e-books both concerning tag recommendation accuracy, diversity as well as a novel semantic similarity metric, which we also propose in this paper.
Original languageEnglish
Title of host publicationREVEAL Workshop co-located with RecSys'2019
Place of PublicationKopenhagen, Denmark
Publication statusPublished - 2019


Dive into the research topics of 'Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metrik'. Together they form a unique fingerprint.

Cite this