In CARSs We Trust: How Context-Aware Recommendations Affect Customers' Trust and Other Business Performance Measures of Recommender Systems

• 成果类型: Article
• 署名作者: Panniello, Umberto; Gorgoglione, Michele; Tuzhilin, Alexander
• 署名单位: Politecnico di Bari; New York University
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2015.0610
• 发表日期: 2016
• 页码: 182-196
• 关键词: E-commerce; INFORMATION; satisfaction; variety; IMPACT

摘要

Most of the work on context-aware recommender systems has focused on demonstrating that the contextual information leads to more accurate recommendations. Little work has been done, however, on studying how much the contextual information affects the business performance. In this paper, we study how including context in recommendations affects customers' trust, sales, and other crucial business-related performance measures. To do this, we delivered content-based and context-aware recommendations through a live controlled experiment with real customers of a commercial European online publisher. We measured the recommendations' accuracy and diversification, how much customers spent purchasing products during the experiment, the quantity and price of their purchases, and the customers' level of trust. We show that collecting and using contextual information in recommendations affects business-related performance measures, such as company sales, by improving the accuracy and diversification of recommendations, which in turn improves trust and, ultimately, business performance results.