TY - JOUR AU - Tay, Yi AU - Zhang, Shuai AU - Luu, Anh Tuan AU - Hui, Siu Cheung AU - Yao, Lina AU - Quang Vinh, Tran Dang PY - 2019/07/17 Y2 - 2024/03/28 TI - Holographic Factorization Machines for Recommendation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v33i01.33015143 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4448 SP - 5143-5150 AB - <p>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 <strong>nine</strong> publicly available datasets for collaborative filtering with explicit feedback. HFM achieves state-of-theart performance on all <strong>nine</strong>, outperforming strong competitors such as Attentional Factorization Machines (AFM) and Neural Matrix Factorization (NeuMF).</p> ER -