Title: Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations,
Abstract: With the popularity of Linked Open Data (LOD) and the associated rise in
freely accessible knowledge that can be accessed via LOD, exploiting LOD for
recommender systems has been widely studied based on various approaches such as
graph-based or using different machine learning models with LOD-enabled
features. Many of the previous approaches require construction of an additional
graph to run graph-based algorithms or to extract path-based features by
combining user- item interactions (e.g., likes, dislikes) and background
knowledge from LOD. In this paper, we investigate Factorization Machines (FMs)
based on particularly lightweight LOD-enabled features which can be directly
obtained via a public SPARQL Endpoint without any additional effort to
construct a graph. Firstly, we aim to study whether using FM with these
lightweight LOD-enabled features can provide competitive performance compared
to a learning-to-rank approach leveraging LOD as well as other well-established
approaches such as kNN-item and BPRMF. Secondly, we are interested in finding
out to what extent each set of LOD-enabled features contributes to the
recommendation performance. Experimental evaluation on a standard dataset shows
that our proposed approach using FM with lightweight LOD-enabled features
provides the best performance compared to other approaches in terms of five
evaluation metrics. In addition, the study of the recommendation performance
based on different sets of LOD-enabled features indicate that property-object
lists and PageRank scores of items are useful for improving the performance,
and can provide the best performance through using them together for FM. We
observe that subject-property lists of items does not contribute to the
recommendation performance but rather decreases the performance.