Blending Conversational Product Advisors and Faceted Filtering in a Graph-Based Approach

Abstract

Today’s e-commerce websites often provide many different components, such as filters and conversational product advisors, to help users find relevant items. However, filters and advisors are often presented separately and treated as independent entities so that the previous input is discarded when users switch between them. This leads to memory loads and disruptions during the search process. In addition, the reasoning behind the advisors’ results is often not transparent. To overcome these limitations, we propose a novel approach that exploits a graph structure to create an integrated system that allows a seamless coupling between filters and advisors. The integrated system utilizes the graph to suggest appropriate filter values and items based on the user’s answers in the advisor. Moreover, it determines follow-up questions based on the filter values set by the user. The interface visualizes and explains the relationship between a given answer and its relevant features to achieve increased transparency in the guidance process. We report the results of an empirical user study with 120 participants that compares the integrated system to a system in which the filtering and advisory mechanisms operate separately. The findings indicate that displaying recommendations and explanations directly in the filter component can increase acceptance and trust in the system. Similarly, combining the advisor with the filters along with the displayed explanations leads to significantly higher levels of knowledge about the relevant product features.