WORDS AND TOPICS: CONTENT REPRESENTATIONS FOR BOOK RECOMMENDATION
The paper describes an exploratory study on content-based book recom-mendation. We use a large dataset of book ratings along with book content. We experiment with several topic modeling variants and tf.idf representation. Predictions based on one of the topic modeling variants slightly outperform a simple baseline of averaged book scores. The obtained results suggest that content features can potentially improve hybrid book recommender systems.