Affiliation:
1. University of Konstanz
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
.
Publisher
Association for Computing Machinery (ACM)
Subject
Artificial Intelligence,Theoretical Computer Science
Reference45 articles.
1. Incorporating contextual information in recommender systems using a multidimensional approach
2. Regression-based latent factor models
3. Context-based splitting of item ratings in collaborative filtering
4. LIBSVM
5. Chen T. Zheng Z. Lu Q. Zhang W. and Yu Y. 2011. Feature-based matrix factorization. Tech. rep. APEX-TR-2011-07-11 Apex Data & Knowledge Management Lab Shanghai Jiao Tong University. Chen T. Zheng Z. Lu Q. Zhang W. and Yu Y. 2011. Feature-based matrix factorization. Tech. rep. APEX-TR-2011-07-11 Apex Data & Knowledge Management Lab Shanghai Jiao Tong University.
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