  Recent model-based Recommender Systems (RecSys) algorithms emphasize on the
use of features, also called side information, in their design similar to
algorithms in Machine Learning (ML). In contrast, some of the most popular and
traditional algorithms for RecSys solely focus on a given user-item-rating
relation without including side information. The goal of this case study is to
provide a performance comparison and assessment of RecSys and ML algorithms
when side information is included. We chose the Movielens-100K data set since
it is a standard for comparing RecSys algorithms. We compared six different
feature sets with varying quantities of features which were generated from the
baseline data and evaluated on a total of 19 RecSys algorithms, baseline ML
algorithms, Automated Machine Learning (AutoML) pipelines, and state-of-the-art
RecSys algorithms that incorporate side information. The results show that
additional features benefit all algorithms we evaluated. However, the
correlation between feature quantity and performance is not monotonous for
AutoML and RecSys. In these categories, an analysis of feature importance
revealed that the quality of features matters more than quantity. Throughout
our experiments, the average performance on the feature set with the lowest
number of features is about 6% worse compared to that with the highest in terms
of the Root Mean Squared Error. An interesting observation is that AutoML
outperforms matrix factorization-based RecSys algorithms when additional
features are used. Almost all algorithms that can include side information have
higher performance when using the highest quantity of features. In the other
cases, the performance difference is negligible (<1%). The results show a clear
positive trend for the effect of feature quantity as well as the important
effects of feature quality on the evaluated algorithms.
