Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity
This work addresses the problem of optimizing recommender systems for researchers and practitioners, but it is incremental as it builds on existing HIN methods.
The study investigated the impact of semantics versus structure in Heterogeneous Information Networks on recommendation accuracy and diversity, finding that structure is crucial while semantic content has limited importance.
The Heterogeneous Information Network (HIN) formalism is very flexible and enables complex recommendations models. We evaluate the effect of different parts of a HIN on the accuracy and the diversity of recommendations, then investigate if these effects are only due to the semantic content encoded in the network. We use recently-proposed diversity measures which are based on the network structure and better suited to the HIN formalism. Finally, we randomly shuffle the edges of some parts of the HIN, to empty the network from its semantic content, while leaving its structure relatively unaffected. We show that the semantic content encoded in the network data has a limited importance for the performance of a recommender system and that structure is crucial.