Temporal proximity filtering

Arun Kumar, Paul R Schrater, Karan Aggarwal

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Users bundle the consumption of their favorite content in temporal proximity to each other, according to their preferences and tastes. Thus, the underlying attributes of items implicitly match user preferences. However, current recommender systems largely ignore this fundamental driver in identifying matching items. In this work, we introduce a novel temporal proximity filtering method to enable items-matching. First, we demonstrate that proximity preferences exist. Second, we present a temporal proximity induced similarity metric driven by user tastes, and third, we show that this induced similarity can be used to learn items pairwise similarity in attribute space. The proposed model does not rely on any knowledge outside users' consumption and provide a novel way to devise user preferences and tastes driven novel items recommender.

Original languageEnglish (US)
Title of host publicationCHI EA 2019 - Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450359719
StatePublished - May 2 2019
Event2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019 - Glasgow, United Kingdom
Duration: May 4 2019May 9 2019

Publication series

NameConference on Human Factors in Computing Systems - Proceedings


Conference2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2019 Copyright held by the owner/author(s).


  • Attributes similarity
  • Music recommendation
  • Proximity filtering
  • Recommender systems
  • Taste model
  • Temporal proximity


Dive into the research topics of 'Temporal proximity filtering'. Together they form a unique fingerprint.

Cite this