Vector-Level and Bit-Level Feature Adjusted Factorization Machine for Sparse Prediction

Yanghong Wu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Junhua Fang, Fuzhen Zhuang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings
EditorsYunmook Nah, Bin Cui, Sang-Won Lee, Jeffrey Xu Yu, Yang-Sae Moon, Steven Euijong Whang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783030594091
StatePublished - 2020
Event25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 - Jeju, Korea, Republic of
Duration: Sep 24 2020Sep 27 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12112 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Database Systems for Advanced Applications, DASFAA 2020
Country/TerritoryKorea, Republic of


  • Factorization machines
  • Feature adjustment
  • Sparse prediction


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