The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Factorization Machines with libFM
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 .
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A Review of Relational Machine Learning for Knowledge Graphs
Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2483592 · Cited by 1,638
Knowledge Graph Convolutional Networks for Recommender Systems
Wang, H., Zhao, M., Xie, X., Li, W., & Guo, M. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313417 · Cited by 1,021
Deep Learning over Multi-field Categorical Data
Zhang, W., Du, T., & Wang, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-30671-1_4 · Cited by 445
Deriving Validity Time in Knowledge Graph
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LCARS: a location-content-aware recommender system
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Metadata
- Authors
- Rendle, S.
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2012-05
- Volume
- 3
- Issue
- 3
- Pages
- 1-22
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W2094286023
- Cited by
- 1,337
- References
- 5
- FWCI
- 129.30
- Type
- journal article
- Language
- en
- Open access
- closed
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