Holographic Factorization Machines for Recommendation


  • Yi Tay Nanyang Technological University
  • Shuai Zhang University of New South Wales
  • Anh Tuan Luu Institute for Infocomm Research
  • Siu Cheung Hui Nanyang Technological University
  • Lina Yao University of New South Wales
  • Tran Dang Quang Vinh Nanyang Technological University




Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks. FMs are characterized by its usage of the inner product of factorized parameters to model pairwise feature interactions, making it highly expressive and powerful. This paper proposes Holographic Factorization Machines (HFM), a new novel method of enhancing the representation capability of FMs without increasing its parameter size. Our approach replaces the inner product in FMs with holographic reduced representations (HRRs), which are theoretically motivated by associative retrieval and compressed outer products. Empirically, we found that this leads to consistent improvements over vanilla FMs by up to 4% improvement in terms of mean squared error, with improvements larger at smaller parameterization. Additionally, we propose a neural adaptation of HFM which enhances its capability to handle nonlinear structures. We conduct extensive experiments on nine publicly available datasets for collaborative filtering with explicit feedback. HFM achieves state-of-theart performance on all nine, outperforming strong competitors such as Attentional Factorization Machines (AFM) and Neural Matrix Factorization (NeuMF).




How to Cite

Tay, Y., Zhang, S., Luu, A. T., Hui, S. C., Yao, L., & Quang Vinh, T. D. (2019). Holographic Factorization Machines for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 5143-5150. https://doi.org/10.1609/aaai.v33i01.33015143



AAAI Technical Track: Machine Learning