"please, not now!" A model for timing recommendations

Nofar Dali Betzalel, Bracha Shapira, Lior Rokach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations


Proactive recommender systems push recommendations to users without their explicit request whenever a recommendation that suits a user is available. These systems strive to optimize the match between recommended items and users' preferences. We assume that recommendations might be reflected with low accuracy not only due to the recommended items' suitability to the user, but also because of the recommendations' timings. We therefore claim that it is possible to learn a model of good and bad contexts for recommendations that can later be integrated in a recommender system. Using mobile data collected during a three week user study, we suggest a two-phase model that is able to classify whether a certain context is at all suitable for any recommendation, regardless of its content. Results reveal that a hybrid model that first decides whether it should use a personal or a non-personal timing model, and then classifies accordingly whether the timing is proper for recommendations, is superior to both the personal or non-personal timing models.

Original languageEnglish
Title of host publicationRecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Number of pages4
ISBN (Electronic)9781450336925
StatePublished - 16 Sep 2015
Event9th ACM Conference on Recommender Systems, RecSys 2015 - Vienna, Austria
Duration: 16 Sep 201520 Sep 2015

Publication series

NameRecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems


Conference9th ACM Conference on Recommender Systems, RecSys 2015


  • Data mining
  • Mobile
  • Personalization
  • Proactivity
  • Recommender systems

ASJC Scopus subject areas

  • Information Systems
  • Software
  • Computer Science Applications
  • Control and Systems Engineering


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