I n this paper, we propose an automated and scalable garment recommender system using real-time in-store videos that can improve the experiences of garment shoppers and increase product sales. The video-based automated recommender (VAR) system is based on observations that garment shoppers tend to try on garments and evaluate themselves in front of store mirrors. Combining state-of-the-art computer vision techniques with marketing models of consumer preferences, the system automatically identifies shoppers' preferences based on their reactions and uses that information to make meaningful personalized recommendations. First, the system uses a camera to capture a shopper's behavior in front of the mirror to make inferences about her preferences based on her facial expressions and the part of the garment she is examining at each time point. Second, the system identifies shoppers with preferences similar to the focal customer from a database of shoppers whose preferences, purchasing, and/or consideration decisions are known. Finally, recommendations are made to the focal customer based on the preferences, purchasing, and/or consideration decisions of these like-minded shoppers. Each of the three steps can be implemented with several variations, and a retailing chain can choose the specific configuration that best serves its purpose. In this paper, we present an empirical test that compares one specific type of VAR system implementation against two alternative, nonautomated personal recommender systems: self-explicated conjoint (SEC) and self-evaluation after try-on (SET). The results show that VAR consistently outperforms SEC and SET. A second empirical study demonstrates the feasibility of VAR in real-time applications. Participants in the second study enjoyed the VAR experience, and almost all of them tried on the recommended garments. VAR should prove to be a valuable tool for both garment retailers and shoppers.Data, as supplemental material, are available at http://dx.

Consumers and marketers use facial information to make important inferences about others in many business contexts. However, consumers and firms are increasingly concerned about privacy and discrimination. To address privacy–perception trade-offs, the authors propose a novel contour-as-face (CaF) framework that transforms face images into contour images incorporating both the nonoutline and outline features of facial parts. In three empirical studies, the authors (1) compare human perceptions of face and contour images along 15 dimensions commonly assessed in marketing contexts; (2) investigate the effectiveness of contour images for protecting anonymity related to identity, age, and gender; and (3) implement the CaF framework in a real-life online dating context. Results show that the CaF framework effectively resolves privacy–perception trade-off problems by preserving the information that is useful for humans to make inferences about many relevant perceptual dimensions in marketing while making it virtually impossible for humans to infer identity and very difficult to infer age and gender accurately—two critical discrimination factors. Results from the field implementation demonstrate the feasibility and value of using the CaF framework for real-life decision making.

No abstract