Forgetting in Knowledge Graph Based Recommender Systems

Recommender systems need to contend with continuous changes in both search spaces and user profiles. The set of items in the search space is usually treated as continuously expanding, however, users also purchase items or change their requirements. This raises the issue of how to”forget” an item after purchase or consumption. This paper addresses the issue of “forgetting” in knowledge graph-based recommender systems. We propose an innovative method for identifying and removing unnecessary or irrelevant triples from the graph itself. Using this approach, we simplify the knowledge graph while maintaining the quality of the recommendations. We also introduce several metrics to assess the impact of forgetting in knowledge graph-based recommender systems. Our experiments demonstrate that incorporating consideration of impact in the forgetting process can enhance the efficiency of the recommender system without compromising the quality of its recommendations.