With customer preference databases growing to colossal sizes, collaborative filtering algorithms run into scalability concerns. By reducing the dimensionality of the input space, we ease the demands of predicting users' tastes for films. A movie-to-movie correlation and distance metric are used to decompose the user-movie rating data into two different movie graphs. Using a Kohonen self-organizing map (SOM), the product space can be divided into meaningful clusters centered on the neurons whose weight vectors are nearest the product weights. The SOM-derived clustering is analyzed via a Principal Components Analysis of the data. The clusterings are then evaluated for their effectiveness via quantitative and qualitative observations on the meaningfulness of the groupings of the films.
['Andrew R. Pariser', 'Willard L. Miranker']
Dimensionality Reduction via Self-Organizing Feature Maps for Collaborative Filtering