Factorization Machines with libFM

By Steffen Rendle. 2012

1,372 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Recommender Systems

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Citations in the corpus

Knowledge Graph Convolutional Networks for Recommender Systems (2019, neutral): “8 • LibFM [14] is a feature-based factorization model in CTR scenarios.”
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018, methodology): “• LibFM [21] is a widely used feature-based factorization model in CTR scenarios.”
DKN: Deep Knowledge-Aware Network for News Recommendation (2018, background): “• LibFM [35] is a state-of-the-art feature-based factorization model and widely used in CTR scenarios.”
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (2017, background): “• LIBFM : LIBFM [18] is a state-of-the-art feature based factorization model.”
Wide & Deep Learning for Recommender Systems (2016, methodology): “…cross-product feature transformations and deep neural networks with dense embeddings is inspired by previous work, such as factorization machines [5] which add generalization to linear models by factorizing the interactions between two variables as a dot product between two low-dimensional…”
Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, neutral): “SVDfeature, libFM) have been developed [9, 31].”
Factorized Q-learning for large-scale multi-agent systems (2018, methodology): “Inspired by the Factorization Machines [18, 19] widely used in recommender systems, we model the complex relationship between the environment and the agents as a high-order high-dimensional tensor and then approximate it through factorization.”
Mean Field Multi-Agent Reinforcement Learning (2018, neutral): “Similar approaches can be found in factorization machine (Rendle, 2012) and learning to rank (Cao et al.”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “Similarly, factorization machines [120] allow to combine latent and observable patterns, by modeling higher order interactions between input variables via low-rank factorizations [78].”
Joint Deep Modeling of Users and Items Using Reviews for Recommendation (2017, methodology): “To model all nested variable interactions in ˆz , we introduce Factorization Machine (FM) [24] as the estimator of the corresponding rating.”
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks (2017, methodology): “0.20 R S E ( FM(train) FM(test) We have the following observations: R M AFMFMLibFM AFM(train)AFM(test)FM(train) 0.40 0.50 AFM(test) 0.45 AFMFM S E And more importantly, tuning the attention network can further improve the generalization of AFM.”
FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction (2019, methodology): “The shallow baseline models include LR(logistic regression)[17], FM[19, 20], FFM[9, 10], AFM[23], and the deep baseline models include FNN[25], DCN[22], DeepFM[4], XDeepFM[15].”
Personalized Reason Generation for Explainable Song Recommendation (2019, methodology): “In offline evaluation, we compare the following methods: FM. Factorization Machines (FM) [64] are widely used in recommendation systems due to their effectiveness and rich functionality.”
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text (2019, methodology): “opts topic distributions to learn latent factors from text reviews. It is a hybrid method. •CDCF, Cross-domain CF with factorization machines (FM) [31], is a cross-domain recommender which extends FM [43]. It is a context-aware approach which applies factorization on the merged domains (aligned by t”
Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks (2017, methodology): “1 of [25] to model the rating prediction task.”
MIND: A Large-scale Dataset for News Recommendation (2020, methodology): “LibFM (Rendle, 2012), a classic recommendation method based on factorization machine.”
Collaborative Denoising Auto-Encoders for Top-N Recommender Systems (2016, positive): “In addition, hierarchical latent factor models [1, 32] and the factorization machine [14] can model interactions between user or item side features.”
Learning Hidden Features for Contextual Bandits (2016, methodology): “Typical solutions include regression-based factor models [2] and factorization machines [20].”
Practical Privacy Preserving POI Recommendation (2020, methodology): “In a traditional centralized setting, all the data and models are kept by the recommender, and FM can be efficiently learned by using gradient descent [45].”