@inproceedings{c63792c5f6d94db2b411590955e57545,
title = "Vector-Level and Bit-Level Feature Adjusted Factorization Machine for Sparse Prediction",
abstract = "Factorization Machines (FMs) are a series of effective solutions for sparse data prediction by considering the interactions among users, items, and auxiliary information. However, the feature representations in most state-of-the-art FMs are fixed, which reduces the prediction performance as the same feature may have unequal predictabilities under different input instances. In this paper, we propose a novel Feature-adjusted Factorization Machine (FaFM) model by adaptively adjusting the feature vector representations from both vector-level and bit-level. Specifically, we adopt a fully connected layer to adaptively learn the weight of vector-level feature adjustment. And a user-item specific gate is designed to refine the vector in bit-level and to filter noises caused by over-adaptation of the input instance. Extensive experiments on two real-world datasets demonstrate the effectiveness of FaFM. Empirical results indicate that FaFM significantly outperforms the traditional FM with a 10.89% relative improvement in terms of Root Mean Square Error (RMSE) and consistently exceeds four state-of-the-art deep learning based models.",
keywords = "Factorization machines, Feature adjustment, Sparse prediction",
author = "Yanghong Wu and Pengpeng Zhao and Yanchi Liu and Sheng, {Victor S.} and Junhua Fang and Fuzhen Zhuang",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 ; Conference date: 24-09-2020 Through 27-09-2020",
year = "2020",
doi = "10.1007/978-3-030-59410-7_27",
language = "English",
isbn = "9783030594091",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "386--402",
editor = "Yunmook Nah and Bin Cui and Sang-Won Lee and Yu, {Jeffrey Xu} and Yang-Sae Moon and Whang, {Steven Euijong}",
booktitle = "Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings",
}