  Recommender systems present a customized list of items based upon user or
item characteristics with the objective of reducing a large number of possible
choices to a smaller ranked set most likely to appeal to the user. A variety of
algorithms for recommender systems have been developed and refined including
applications of deep learning neural networks. Recent research reports point to
a need to perform carefully controlled experiments to gain insights about the
relative accuracy of different recommender algorithms, because studies
evaluating different methods have not used a common set of benchmark data sets,
baseline models, and evaluation metrics. This investigation used publicly
available sources of ratings data with a suite of three conventional
recommender algorithms and two deep learning (DL) algorithms in controlled
experiments to assess their comparative accuracy. Results for the non-DL
algorithms conformed well to published results and benchmarks. The two DL
algorithms did not perform as well and illuminated known challenges
implementing DL recommender algorithms as reported in the literature. Model
overfitting is discussed as a potential explanation for the weaker performance
of the DL algorithms and several regularization strategies are reviewed as
possible approaches to improve predictive error. Findings justify the need for
further research in the use of deep learning models for recommender systems.
