Unified and Scalable Incremental Recommenders with Consumed Item Packs

  • Rachid Guerraoui
  • Erwan Le MerrerEmail author
  • Rhicheek Patra
  • Jean-Ronan Vigouroux
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11725)


Recommenders personalize the web content using collaborative filtering to relate users (or items). This work proposes to unify user-based, item-based and neural word embeddings types of recommenders under a single abstraction for their input, we name Consumed Item Packs (CIPs). In addition to genericity, we show this abstraction to be compatible with incremental processing, which is at the core of low latency recommendation to users. We propose three such algorithms using CIPs, analyze them, and describe their implementation and scalability for the Spark platform. We demonstrate that all three provide a recommendation quality that is competitive with three algorithms from the state-of-the-art.


Implicit recommenders Incremental updates Parallelism Spark 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rachid Guerraoui
    • 1
  • Erwan Le Merrer
    • 2
    Email author
  • Rhicheek Patra
    • 3
  • Jean-Ronan Vigouroux
    • 4
  1. 1.EPFLLausanneSwitzerland
  2. 2.Univ Rennes, Inria, CNRS, IRISARennesFrance
  3. 3.Oracle LabsZurichSwitzerland
  4. 4.TechnicolorRennesFrance

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