XTransE: Explainable Knowledge Graph Embedding for Link Prediction with Lifestyles in e-Commerce

In e-Commerce, we are interested in deals by lifestyle which will improve the diversity of items shown to users. A lifestyle, an important motivation for consumption, is a person’s pattern of living in the world as expressed in activities, interests, and opinions. In this paper, we focus on the key task for deals by lifestyle, establishing linkage between items and lifestyles. We build an item-lifestyle knowledge graph to fully utilize the information about them and formulate it as a knowledge graph link prediction task. A lot of knowledge graph embedding methods are proposed to accomplish relational learning in academia. Although these methods got impressive results on benchmark datasets, they can’t provide insights and explanations for their prediction which limit their usage in industry. In this scenario, we concern about not only linking prediction results, but also explanations for predicted results and human-understandable rules, because explanations help us deal with uncertainty from algorithms and rules can be easily transferred to other platforms. Our proposal includes an explainable knowledge graph embedding method (XTransE), an explanation generator and a rule collector, which outperforms traditional classifier models and original embedding method during prediction, and successfully generates explanations and collects meaningful rules.