Ni Huang’s research while affiliated with University of Miami and other places

Reciprocal recommendation systems are crucial for online dating platforms to provide quality matches and reduce choice overload. However, the design of reciprocal recommendation systems grapples with the challenges of estimating interpersonal compatibility and predicting the likelihood that two prospective partners will accept each other. Furthermore, despite the crucial role of users’ linguistic styles in determining user match decision-making, the contemporary design of such recommendation systems has not yet effectively incorporated this information. To bridge these gaps, we develop an end-to-end personalized Linguistic Style Matching-based Reciprocal Recommendation System (LS-RRS). We propose cross-user and within-user contrastive learning strategies combined with random masking to extract users’ linguistic styles, and further integrate visual and textual information using an efficient convolution block. LS-RRS further models the matching probability using a conditional probability function and introduces a preference inflation factor on the receiver side to account for the asymmetric roles of the bilateral sides. The proposed model addresses the challenge of incorporating users’ linguistic styles into reciprocal recommendation and details the modeling of the two-stage matching process. Extensive experiments show that LS-RRS outperforms state-of-the-art models in recommendation performance, with a 29.35% increase in NDCG@10 when incorporating linguistic styles. Our follow-up analyses further validate the importance and effectiveness of the linguistic style extraction design through word level and sentence level visualizations, as well as qualitative case studies. This research contributes to the literature on reciprocal recommendation and offers a viable solution for alleviating user choice overload on online dating platforms.

Livestream selling is an innovative form of online shopping that supports real-time interactions between streamers and consumers. However, a key challenge remains: Streamers have limited capacity to answer individual inquiries, whereas shoppers expect fast, personalized responses. This study investigates whether an AI-powered streaming assistant can address this tension by providing interactive, chat-based support to help consumers access and process information. Through a large-scale randomized field experiment on a leading livestream selling platform, we find that the AI assistant increases sales by 3.00% and reduces product return rates by 12.55%. Our analysis suggests that the AI assistant helps consumers feel more informed and confident in their purchases, thereby reducing uncertainty. At the same time, the AI assistant can occasionally disrupt the consumers’ livestream experience. Overall, the benefits of uncertainty reduction outweigh the negative influence of interruptions. For platform managers and policymakers, these findings evidence the potential of AI technology to enhance online commerce. The AI assistant is particularly effective for high-uncertainty products and for streamers with large audiences, offering implications for strategic deployment. Our research provides actionable insights for integrating AI into livestream selling and other digital commerce scenarios where real-time, AI-powered support can facilitate both consumer satisfaction and business growth.

In the dynamic world of online dating, a key challenge faced by platforms is the cold-start problem, where newly matched users are hesitant to engage due to privacy concerns. Our solution, ephemeral sharing, addresses this by balancing privacy with the need for personal information sharing. This feature allows personal photos to disappear and become untraceable soon after being viewed, reassuring users about their privacy. We conducted a large-scale randomized experiment with more than 70,000 users to evaluate the impact of ephemeral sharing. The results are compelling: users who could share ephemeral photos were more likely to send personal images alongside with their matching request, especially those with human faces, leading to more matches and higher engagement. Significantly, this effect was more pronounced among users who are more sensitive to their privacy. Furthermore, ephemeral sharing was found to reduce users’ concerns related to data collection, dissemination, and identity misuse, thereby increasing the willingness to share personal information. This approach not only enhances user privacy but also stimulates more active engagement on the platform. For dating platforms and similar platforms, adopting ephemeral sharing can revolutionize user experience. It provides a strategic advantage by boosting user personal information sharing and enhancing privacy, crucial for maintaining meaningful communication in online dating. This feature represents a significant step forward in designing user-centric, privacy-conscious platforms.

Online learners often experience a lack of sustained motivation given the self-paced nature of online learning, resulting in inefficiency and a high dropout rate. It is important to explore options that help users optimize their learning behavior and improve their learning performance. Using a multimethod approach, we show that (a) starting learning sessions at on-the-hour time points activates users’ implemental mindset, which supports them in building greater learning persistence and achieving better learning performance, and (b) social presence significantly attenuates the effects of on-the-hour time points in online learning. Based on our findings, we suggest that both learners and instructors on online learning platforms can leverage common temporal cues, such as on-the-hour time points, to schedule learning activities in order to motivate online learners, enhance their learning persistence, and improve their learning performance. Additionally, online learning platforms can also adopt designs that facilitate virtual connections among geographically separated users to enhance their learning productivity.

Voice‐based artificial intelligence (AI) systems have been recently deployed to replace traditional interactive voice response (IVR) systems in call center customer service. However, there is little evidence that sheds light on how the implementation of AI systems impacts customer behavior, as well as AI systems’ effects on call center customer service performance. By leveraging the proprietary data obtained from a natural field experiment in a large telecommunication company, we examine how the introduction of a voice‐based AI system affects call length, customers’ demand for human service, and customer complaints in call center customer service. We find that the implementation of the AI system temporarily increases the duration of machine service and customers’ demand for human service; however, it persistently reduces customer complaints. Furthermore, our results reveal interesting heterogeneity in the effectiveness of the voice‐based AI system. For relatively simple service requests, the AI system reduces customer complaints for both experienced and inexperienced customers. However, for complex requests, customers appear to learn from the prior experience of interacting with the AI system, which leads to fewer complaints. Moreover, the AI‐based system has a significantly larger effect on reducing customer complaints for older and female customers as well as for customers who have had extensive experience using the IVR system. Finally, we find that the speech‐recognition failures in customer‐AI interactions lead to increases in customers’ demand for human service and customer complaints. The results from this study provide implications for the implementation of an AI system in call center operations. This article is protected by copyright. All rights reserved