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={RecSys '14},
  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.Expand
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56 Citations
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56 Citations

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BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation
TLDR
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. Expand
Learning to context-aware recommend with hierarchical factorization machines
TLDR
This study considers the problem of exploiting hierarchical structures to improve recommendation quality and proposes a novel two-stage recommendation model called Hierarchical Factorization Machines (HFM), which is efficient and effective. Expand
Feature Selection for FM-Based Context-Aware Recommendation Systems
TLDR
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. Expand
A Boosting Framework of Factorization Machine
TLDR
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. Expand
Synergies that Matter: Efficient Interaction Selection via Sparse Factorization Machine
TLDR
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. Expand
Enhanced factorization machine via neural pairwise ranking and attention networks
TLDR
A new variant of NPRFM is proposed, which learns the importance of feature interactions by introducing the attention mechanism, and outperforms the traditional factorization machine models. Expand
Memory-aware gated factorization machine for top-N recommendation
TLDR
A memory-aware gated factorization machine (MAGFM), which improves the FM method by introducing two new components: an external user memory matrix is introduced to each user, which can enrich the representation capacity by leveraging user historical items and the auxiliary information associated with the historical items. Expand
L G ] 1 7 A pr 2 01 8 A Boosting Framework of Factorization Machine
Recently, Factorization Machines (FM) has become more and more popular for recommendation systems, due to its effectiveness in finding informative interactions between features. Usually, the weights… Expand
DS-FACTO: Doubly Separable Factorization Machines
TLDR
A hybrid-parallel stochastic optimization algorithm DS-FACTO is proposed, which partitions both the data as well as parameters of the factorization machine simultaneously and is fully de-centralized and does not require the use of any parameter servers. Expand
Advanced Factorization Models for Recommender Systems
TLDR
The majority of the techniques that are proposed in this thesis are based on state-of-the-art factorization models known as Factorization Machines (FMs), a class of recommender system algorithms that learn user preferences based on a method called factorization. Expand
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References

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