Improved Neighborhood-Based Algorithms for Large-Scale Recommender
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
accuracy. The proposed methods are tested on the Netflix dataset.
Reference: A. Toescher, M. Jahrer, and R. Legenstein.
Improved neighborhood-based algorithms for large-scale recommender systems.
In KDD-Cup and Workshop. ACM, 2008.