Transfer Learning for Item Recommendations and Knowledge Graph Completion in Item Related Domains via a Co-Factorization Model

With the popularity of Knowledge Graphs (KGs) in recent years, there have been many studies that leverage the abundant background knowledge available in KGs for the task of item recommendations. However, little attention has been paid to the incompleteness of KGs when leveraging knowledge from them. In addition, previous studies have mainly focused on exploiting knowledge from a KG for item recommendations, and it is unclear whether we can exploit the knowledge in the other way, i.e, whether user-item interaction histories can be used for improving the performance of completing the KG with regard to the domain of items. In this paper, we investigate the effect of knowledge transfer between two tasks: (1) item recommendations, and (2) KG completion, via a co-factorization model (CoFM) which can be seen as a transfer learning model. We evaluate CoFM by comparing it to three competitive baseline methods for each task. Results indicate that considering the incompleteness of a KG outperforms a state-of-the-art factorization method leveraging existing knowledge from the KG, and performs better than other baselines. In addition, the results show that exploiting user-item interaction histories also improves the performance of completing the KG with regard to the domain of items, which has not been investigated before