Incorporating dwell time in session-based recommendations with recurrent Neural networks

Veronika Bogina, Tsvi Kuflik

Research output: Contribution to journalConference articlepeer-review


Recurrent Neural Networks (RNN) is a frequently used technique for sequence data predictions. Recently, it gains popularity in the Recommender Systems domain, especially for session-based recommendations where naturally, each session is defined as a sequence of clicks, and timestamped data per click is available. In our research, in its early stages, we explore the value of incorporating dwell time into existing RNN framework for session-based recommendations by boosting items above the predefined dwell time threshold. We show improvement in recall@20 and MRR@20 by evaluating the proposed approach on e-commerce RecSys’15 challenge dataset.

Original languageEnglish
Pages (from-to)57-59
Number of pages3
JournalCEUR Workshop Proceedings
StatePublished - 2017
Event1st Workshop on Temporal Reasoning in Recommender Systems, RecTemp 2017 - Como, Italy
Duration: 27 Aug 201731 Aug 2017

Bibliographical note

Publisher Copyright:
Copyright © 2017 for this paper by its authors.


  • Deep learning
  • Dwell time
  • Recommender systems
  • Recurrent neural networks
  • Temporal aspects

ASJC Scopus subject areas

  • General Computer Science


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