Genre prediction to inform the recommendation process
Nevena Dragovic and Maria Soledad Pera. 2016. “Genre prediction to inform the recommendation process”. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys 2016 Poster Proceedings).
Abstract
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 counter-part.