Gradient boosting factorization machines
@inproceedings{Cheng2014GradientBF, title={Gradient boosting factorization machines}, author={Chen-Kuang Cheng and Fen Xia and T. Zhang and Irwin King and Michael R. Lyu}, booktitle={ACM Conference on Recommender Systems}, year={2014} }
Recommendation techniques have been well developed in the past decades. [] Key Method Then we propose a novel Gradient Boosting Factorization Machine (GBFM) model to incorporate feature selection algorithm with Factorization Machines into a unified framework. The experimental results on both synthetic and real datasets demonstrate the efficiency and effectiveness of our algorithm compared to other state-of-the-art methods.
70 Citations
BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation
- Computer ScienceIUI
- 2017
BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme, which outperforms a number of state-of-the-art approaches for top-N recommendation.
Learning to context-aware recommend with hierarchical factorization machines
- Computer ScienceInf. Sci.
- 2017
Feature Selection for FM-Based Context-Aware Recommendation Systems
- Computer Science2017 IEEE International Symposium on Multimedia (ISM)
- 2017
This paper proposes a framework to automatically select features on FM-based recommender systems to improve the prediction quality and considers the density of the important features in the binary space to rank and select the relevant features inThe original data.
Influence of Auxiliary Features in Factorization-based Collaborative Filtering
- Computer Science
- 2016
The results show that the optimization of hyper-parameters, factorization dimensionality and feature weights is a very crucial part of using FMs for recommendations and there is no clear answer to which auxiliary features are useful, as it depends heavily on the dataset.
A Boosting Framework of Factorization Machine
- Computer ScienceInt. J. Pattern Recognit. Artif. Intell.
- 2021
This work proposes an Adaptive Boosting framework of Factorization Machines (AdaFM), which can adaptively search for proper ranks for different datasets without re-training, and will adaptively gradually increases its rank according to its performance until the performance does not grow, using boosting strategy.
Synergies that Matter: Efficient Interaction Selection via Sparse Factorization Machine
- Computer ScienceSDM
- 2016
An efficient Sparse Factorization Machine (SFM) is proposed, that simultaneously identifies relevant user and item content features, models interactions between these relevant features, and learns a bilinear model using only these synergistic interactions.
Enhanced factorization machine via neural pairwise ranking and attention networks
- Computer SciencePattern Recognit. Lett.
- 2020
Enhancing Factorization Machines With Generalized Metric Learning
- Computer ScienceIEEE Transactions on Knowledge and Data Engineering
- 2022
A Mahalanobis distance and a deep neural network methods, which can effectively model the linear and non-linear correlations between features, respectively, are presented and an efficient approach for simplifying the model functions is designed.
Memory-aware gated factorization machine for top-N recommendation
- Computer ScienceKnowl. Based Syst.
- 2020
L G ] 1 7 A pr 2 01 8 A Boosting Framework of Factorization Machine
- Computer Science
- 2018
This work proposes an Adaptive Boosting framework of Factorization Machines (AdaFM), which can adaptively search for proper ranks for different datasets without re-training, and will adaptively gradually increases its rank according to its performance until the performance does not grow, using boosting strategy.
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