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

TL;DR

This paper introduces a hybrid tag recommendation system for e-books that combines user search queries and editor tags, utilizing a new semantic similarity metric to improve accuracy and diversity.

Contribution

The paper proposes a novel hybrid approach for e-book tag recommendation that incorporates user search behavior and introduces a new semantic similarity metric.

Findings

Improved tag recommendation accuracy

Enhanced diversity of recommended tags

Introduction of a new semantic similarity metric

Abstract

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.