  Modeling users for the purpose of identifying their preferences and then
personalizing services on the basis of these models is a complex task,
primarily due to the need to take into consideration various explicit and
implicit signals, missing or uncertain information, contextual aspects, and
more. In this study, a novel generic approach for uncovering latent preference
patterns from user data is proposed and evaluated. The approach relies on
representing the data using graphs, and then systematically extracting
graph-based features and using them to enrich the original user models. The
extracted features encapsulate complex relationships between users, items, and
metadata. The enhanced user models can then serve as an input to any
recommendation algorithm. The proposed approach is domain-independent
(demonstrated on data from movies, music, and business recommender systems),
and is evaluated using several state-of-the-art machine learning methods, on
different recommendation tasks, and using different evaluation metrics. The
results show a unanimous improvement in the recommendation accuracy across
tasks and domains. In addition, the evaluation provides a deeper analysis
regarding the performance of the approach in special scenarios, including high
sparsity and variability of ratings.
