Abstract
Personalization algorithms recommend products to users based on their
previous interactions with the system. The products could be books,
movies, or products in a retail system. The earliest personalization
algorithms were based on factorization of the user-item matrix where
each entry in the matrix would correspond to an interaction, or absence
of an interaction of the user with the product. In this article, we
compare three recently developed personalization algorithms. The three
algorithms are Bayesian Personalized Ranking, Taxonomy Discovery for
Personalized Recommendations and Multi-Matrix Factorization. We compare
the three algorithms on the hit rate @ position 10 on a held out test
set on 1 million users and 200 thousand items in the catalog of Target
Corporation. We report our findings in table 1. We develop all three
algorithms on an Apache Spark parallel implementation.