Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric
This work addresses the specific problem of e-book annotation for publishers and editors, representing an incremental improvement in tag recommendation systems.
The authors tackled the problem of recommending descriptive tags for e-books by developing a hybrid system that combines Amazon user search queries with publisher metadata, evaluating 19 algorithms. Their approach improved tag recommendation accuracy and diversity, as measured by a novel semantic similarity metric they introduced.
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.