Together with HGV, we developed a tag recommender system to support the e-book annotation process. Therefore, we proposed 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. In total, we implemented and evaluated 19 algorithms and our results show that we can improve the performance of tag recommender systems for e-books both concerning tag recommendation accuracy, diversity and a novel semantic similarity metric.

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Findings of the project were accepted at the REVEAL workshop of RecSys'2019

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