Knowledge Graph-Based Integration of Conversational Advisors and Faceted Filtering

Modern e-commerce websites often provide users with a variety of components, such as faceted filters and conversational recommender systems, that act as product advisors to help them find relevant products. However, these components are often treated separately and presented as independent components, leading to increased cognitive load and disruption in the search process. Also, the reasoning behind the resulting product recommendations is often not transparent. To address these limitations, we propose a novel approach that relies on a knowledge graph structure to seamlessly integrate faceted filtering and conversational advisors based on graphical user interfaces (GUI). Concretely, the knowledge graph is used to suggest filter values and products based on the user’s answers in the advisor, and, conversely, to determine follow-up questions based on the user’s selected filter values. The user interface also visualizes and explains the underlying relationships between answers given to the advisor and relevant product features in the filter component in order to increase the transparency of the search process. We conducted two user studies with a total of 448 participants to compare a system that integrates the different components according to our approach with a baseline system in which the mechanisms operate separately. Sequence analysis of the logged interaction data provided insights into participants’ behavior as they interacted with both systems. The results indicate that displaying recommended products and related explanations directly in the filter component increases acceptance and trust in the system. Also, the combination of a conversational advisor with values displayed in a filter interface, along with explanations of the underlying relationships, significantly contributes to the knowledge and understanding of those product features that are important in terms of the current search goal.