Understanding the Latent Features of Matrix Factorization Algorithms in Movie Recommender Systems
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چکیده
Recommender systems are most often approached from a number-crunching perspective, even though recent algorithms are very similar to methods used in psychology. Based on these similarities this study tried to investigate if the prediction models produced by a matrix factorization algorithm can be interpreted like is done in more psychological studies. Multidimensional scaling was used on data gathered via an online cardsort to establish the psychological attributes people perceive in movies. These attributes were then partially retrieved in the matrix factorization model.
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تاریخ انتشار 2011