Genre prediction to inform the recommendation process

Nevena Dragovic, Maria Soledad Pera

Research output: Contribution to journalConference articleScientificpeer-review


In this paper we present a time-based genre prediction strategy that can inform the book recommendation process. To explicitly consider time in predicting genres of interest, we rely on a popular time series forecasting model as well as reading patterns of each individual reader or group of readers (in case of libraries or publishing companies). Based on a conducted initial assessment using the Amazon dataset, we demonstrate our strategy outperforms its baseline counterpart.

Original languageEnglish
JournalCEUR Workshop Proceedings
Publication statusPublished - 2016
Externally publishedYes
Event10th ACM Conference on Recommender Systems, RecSys 2016 - MIT, Boston, MA, United States
Duration: 15 Sep 201619 Sep 2016


  • Books
  • Genre
  • Prediction
  • Time Sequence


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