Document Type

Conference Proceeding

Publication Date



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.

Copyright Statement

This document was originally published in Proceedings of the Poster Track of the 10th ACM Conference on Recommender Systems, RecSys 2016 by CEUR-WS. Copyright restrictions may apply.