Field-Aware Factorization Machines (FFMs) Algorithm
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A Field-Aware Factorization Machines (FFMs) Algorithm is a Factorization Machines algorithm that ...
- Context:
- It can be represented by [math]\displaystyle{ \phi(\mathbf{w},\mathbf{x}) = ∑_{j_1, j_2∈C_2}〈\mathbf{w}_{j_1, f_1},\mathbf{w}_{j_2, f_2}〉x_{j_1} x_{j_2} }[/math] (plus bias terms), where $\mathbf{w}_{j_1}$ and $\mathbf{w}_{j_1}$ are two vectors with (user-defined) length $k$.
- It can be implemented by a Field-Aware Factorization Machine-based System.
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- Example(s):
- (Juan et al., 2016).
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- Counter-Example(s):
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- See: Sparse High-Dimensional Data, CTR Modeling Algorithm.
References
2016
- (Juan et al., 2016) ⇒ Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. (2016). “Field-aware Factorization Machines for CTR Prediction.” In: Proceedings of the 10th ACM Conference on Recommender Systems. ISBN:978-1-4503-4035-9 doi:10.1145/2959100.2959134
- QUOTE: Click-through rate (CTR) prediction plays an important role in computational advertising. Models based on degree-2 polynomial mappings and factorization machines (FMs) are widely used for this task. Recently, a variant of FMs, field-aware factorization machines (FFMs), outperforms existing models in some world-wide CTR-prediction competitions. Based on our experiences in winning two of them, in this paper we establish FFMs as an effective method for classifying large sparse data including those from CTR prediction. First, we propose efficient implementations for training FFMs. Then we comprehensively analyze FFMs and compare this approach with competing models. Experiments show that FFMs are very useful for certain classification problems. Finally, we have released a package of FFMs for public use.