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An Intelligent Field-Aware Factorization Machine Model

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Book cover Database Systems for Advanced Applications (DASFAA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10177))

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Abstract

The widely-used field-aware factorization machines model (FFM) takes the interactions of all the text features into consideration which will lead to a large number of invalid calculations. An intelligent field-aware factorization machine model (iFFM) is proposed in this paper. In the model, the key attributes are promoted and the factor selection operations are embedded into the computation process intelligently by using the auto feature engineering technology. Meanwhile, Markov Chain Monte Carlo (MCMC) and stochastic gradient descent (SGD) methods are applied to optimize the loss function and improve the recommendation accuracy. In order to get better diversity, a new model iFFM-2 is put forward, which is the linear weighted combination of iFFM and a model built based on the heat-spreading algorithm. The experimental results show that iFFM can obtain higher accuracy and computation efficiency compared with FFMs, iFFM-2 inherits the accuracy of iFFM, and it can provide better diversity.

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Notes

  1. 1.

    https://www.kaggle.com/c/criteo-display-ad-challenge .

  2. 2.

    https://www.kaggle.com/c/avazu-ctr-prediction .

  3. 3.

    https://github.com/turi-code/python-libffm .

  4. 4.

    https://en.wikipedia.org/wiki/One-hot .

  5. 5.

    http://webscope.sandbox.yahoo.com/myrequests.php .

  6. 6.

    http://grouplens.org/datasets/movielens/1m/ .

  7. 7.

    http://www.kddcup2012.org/c/kddcup2012-track2/data .

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Authors and Affiliations

  1. School of Computer Science and Technology, Donghua University, Shanghai, 201620, China

    Cairong Yan, Qinglong Zhang, Xue Zhao & Yongfeng Huang

Authors
  1. Cairong Yan
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  2. Qinglong Zhang
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  3. Xue Zhao
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  4. Yongfeng Huang
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Corresponding author

Correspondence to Cairong Yan .

Editor information

Editors and Affiliations

  1. Arizona State University , Tempe - Phoenix, Arizona, USA

    Selçuk Candan

  2. Hong Kong University of Science and Tech , Hong Kong, China

    Lei Chen

  3. Aalborg University , Aalborg, Denmark

    Torben Bach Pedersen

  4. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

  5. The University of Queensland , Brisbane, Queensland, Australia

    Wen Hua

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Yan, C., Zhang, Q., Zhao, X., Huang, Y. (2017). An Intelligent Field-Aware Factorization Machine Model. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-55753-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55752-6

  • Online ISBN: 978-3-319-55753-3

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