Incorporating Impressions to Graph-Based Recommenders

Graph-based approaches have become an effective strategy to model the users’ preferences in recommender systems accurately; however, despite their excellent recommendation quality, the literature still needs to incorporate impressions (past recommendations) into existing approaches. By their definition, impressions contain the selection of the most relevant items for the user; enriching the users’ profiles with those items may lead to higher-quality recommendations. In this work, we propose and empirically explore the effectiveness of two approaches that include impressions into graph-based recommenders. Both approaches are simple yet extensible as they do not change the definitions of the recommenders; but transform their main data structure: the graph’s adjacency matrix. The results of our experiments suggest that our approaches may improve the recommendation quality of graph-based recommenders that do not use impressions; however, we also find that beyond-accuracy metrics may become negatively affected.