WORDS AND TOPICS: CONTENT REPRESENTATIONS FOR BOOK RECOMMENDATION

P. 63–71.

Kolesnichenko L., Braslavski P.

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.

Keywords: book recommendation

In book

Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог» (Москва, 29 мая — 1 июня 2019 г.)

Вып. 18. , М.: Издательский центр «Российский государственный гуманитарный университет», 2019.