publication . Article . Conference object . 2008

Improved neighborhood-based algorithms for large-scale recommender systems

Andreas Töscher; Michael Jahrer; Robert Legenstein;
Closed Access
  • Published: 24 Aug 2008
Abstract
Neighborhood-based algorithms are frequently used modules of recommender systems. Usually, the choice of the similarity measure used for evaluation of neighborhood relationships is crucial for the success of such approaches. In this article we propose a way to calculate similarities by formulating a regression problem which enables us to extract the similarities from the data in a problem-specific way. Another popular approach for recommender systems is regularized matrix factorization (RMF). We present an algorithm -- neighborhood-aware matrix factorization -- which efficiently includes neighborhood information in a RMF model. This leads to increased prediction...
Subjects
free text keywords: Matrix decomposition, Collaborative filtering, Regression problems, Machine learning, computer.software_genre, computer, Algorithm, Data mining, Recommender system, Similarity measure, Computer science, Artificial intelligence, business.industry, business, Similarity matrix
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publication . Article . Conference object . 2008

Improved neighborhood-based algorithms for large-scale recommender systems

Andreas Töscher; Michael Jahrer; Robert Legenstein;