Journal ArticleDOI
Factorization Machines with libFM
TLDR
The libFM as mentioned in this paper tool is a software implementation for factorization machines that features stochastic gradient descent (SGD) and alternating least-squares (ALS) optimization, as well as Bayesian inference using Markov Chain Monto Carlo (MCMC).Abstract:
Factorization approaches provide high accuracy in several important prediction problems, for example, recommender systems. However, applying factorization approaches to a new prediction problem is a nontrivial task and requires a lot of expert knowledge. Typically, a new model is developed, a learning algorithm is derived, and the approach has to be implemented.Factorization machines (FM) are a generic approach since they can mimic most factorization models just by feature engineering. This way, factorization machines combine the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain. libFM is a software implementation for factorization machines that features stochastic gradient descent (SGD) and alternating least-squares (ALS) optimization, as well as Bayesian inference using Markov Chain Monto Carlo (MCMC). This article summarizes the recent research on factorization machines both in terms of modeling and learning, provides extensions for the ALS and MCMC algorithms, and describes the software tool libFM.read more
Citations
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Proceedings ArticleDOI
Wide & Deep Learning for Recommender Systems
Heng-Tze Cheng,Levent Koc,Jeremiah Harmsen,Tal Shaked,Tushar Deepak Chandra,Hrishi Aradhye,Glen Anderson,Greg S. Corrado,Wei Chai,Mustafa Ispir,Rohan Anil,Zakaria Haque,Lichan Hong,Vihan Jain,Xiaobing Liu,Hemal Shah +15 more
TL;DR: Wide & Deep learning is presented---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems and is open-sourced in TensorFlow.
Journal ArticleDOI
A Review of Relational Machine Learning for Knowledge Graphs
TL;DR: This paper provides a review of how statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph) and how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web.
Proceedings ArticleDOI
Collaborative Knowledge Base Embedding for Recommender Systems
TL;DR: A heterogeneous network embedding method is adopted, termed as TransR, to extract items' structural representations by considering the heterogeneity of both nodes and relationships and a final integrated framework, which is termed as Collaborative Knowledge Base Embedding (CKE), to jointly learn the latent representations in collaborative filtering.
Posted Content
Wide & Deep Learning for Recommender Systems
Heng-Tze Cheng,Levent Koc,Jeremiah Harmsen,Tal Shaked,Tushar Deepak Chandra,Hrishi Aradhye,Glen Anderson,Greg S. Corrado,Wei Chai,Mustafa Ispir,Rohan Anil,Zakaria Haque,Lichan Hong,Vihan Jain,Xiaobing Liu,Hemal Shah +15 more
TL;DR: Wide & Deep as mentioned in this paper combines the benefits of memorization and generalization for recommender systems by jointly trained wide linear models and deep neural networks, which can generalize better to unseen feature combinations through lowdimensional dense embeddings learned for the sparse features.
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References
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Journal ArticleDOI
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