A Framework for Time-Aware Recommendations

  • Kostas Stefanidis
  • Irene Ntoutsi
  • Kjetil Nørvåg
  • Hans-Peter Kriegel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7447)

Abstract

Recently, recommendation systems have received significant attention. However, most existing approaches focus on recommending items of potential interest to users, without taking into consideration how temporal information influences the recommendations. In this paper, we argue that time-aware recommendations need to be pushed in the foreground. We introduce an extensive model for time-aware recommendations from two perspectives. From a fresh-based perspective, we propose using a suite of aging schemes towards making recommendations mostly depend on fresh and novel user preferences. From a context-based perspective, we focus on providing different suggestions under different temporal specifications. The proposed strategies are experimentally evaluated using real movies ratings.

Keywords

Recommendation System Domain Expert User Preference Mean Absolute Error Temporal Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kostas Stefanidis
    • 1
  • Irene Ntoutsi
    • 2
  • Kjetil Nørvåg
    • 1
  • Hans-Peter Kriegel
    • 2
  1. 1.Department of Computer and Information ScienceNorwegian University of Science and TechnologyTrondheimNorway
  2. 2.Institute for InformaticsLudwig Maximilian UniversityMunichGermany

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