How Neighborhood Exploration influences Novelty and Diversity in Graph Collaborative Filtering

Graph convolutional networks (GCNs) have recently been shown to improve the recommendation accuracy of collaborative filtering algorithms. Their message-passing schema refines user and item node representation by aggregating the informative content from the neighborhood. However, noisy contributions can flatten the differences among nodes after multiple hops, as not all user-item interactions are equally important. This impact is mitigated by (i) restricting the exploration depth in the graph and optionally weighting the neighbor contribution and (ii) going beyond the traditional message propagation at multiple hops. Nevertheless, it remains unclear how these exploration strategies affect the recommendation of novel and diverse products. This study investigates the influence of such GCN techniques on novelty and diversity of recommendations. It also assesses and motivates the impact of the number of exploration hops on the same metrics by analyzing interactions between same-type and different-type nodes, such as user-user and user-item. Code and datasets are available at: https://github.com/sisinflab/Novelty-Diversity-Graph.