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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2012: Machine Learning and Knowledge Discovery in Databases pp 67–82Cite as

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Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Implicit Feedback

Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Implicit Feedback

  • Balázs Hidasi21,22 &
  • Domonkos Tikk21 
  • Conference paper
  • 5589 Accesses

  • 55 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7524)

Abstract

Albeit the implicit feedback based recommendation problem—when only the user history is available but there are no ratings—is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few implicit feedback benchmark datasets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALS applies a fast, ALS-based tensor factorization learning method that scales linearly with the number of non-zero elements in the tensor. The method also allows us to incorporate various contextual information into the model while maintaining its computational efficiency. We present two context-aware implementation variants of iTALS. The first incorporates seasonality and enables to distinguish user behavior in different time intervals. The other views the user history as sequential information and has the ability to recognize usage pattern typical to certain group of items, e.g. to automatically tell apart product types that are typically purchased repetitively or once. Experiments performed on five implicit datasets (LastFM 1K, Grocery, VoD, and “implicitized” Netflix and MovieLens 10M) show that by integrating context-aware information with our factorization framework into the state-of-the-art implicit recommender algorithm the recommendation quality improves significantly.

Keywords

  • recommender systems
  • tensor factorization
  • context awareness
  • implicit feedback

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Author information

Authors and Affiliations

  1. Gravity R&D Ltd., Hungary

    Balázs Hidasi & Domonkos Tikk

  2. Budapest University of Technology and Economics, Hungary

    Balázs Hidasi

Authors
  1. Balázs Hidasi
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  2. Domonkos Tikk
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Editor information

Editors and Affiliations

  1. Intelligent Systems Laboratory, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Bristol, UK

    Peter A. Flach

  2. Intelligent Systems Laboratory, University of Bristol, Merchant Venturers Building, Woodland Road,, BS8 1UB, Bristol, UK

    Tijl De Bie & Nello Cristianini & 

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Hidasi, B., Tikk, D. (2012). Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Implicit Feedback. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33486-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-33486-3_5

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