AI versus Friends in Recommending Online Content: A Large-scale Field Experiment
2023-12-15 10:56:45
Content platforms rely intensively on artificial intelligence (AI) or social networks (friends) to recommend personalized online content. Nevertheless, little is known about the different impacts of these two dominant recommendation mechanisms on user engagement with the content and the platform.  We, therefore, conduct a large-scale randomized field experiment that involves over 2.1 million users and over 6.8 million items (online content) recommended by AI or friends (i.e., contacts) on WeChat. We randomly assign users into three groups: Users in Group I are exposed to the content recommended by AI only; those in Group II are exposed primarily to the content recommended by friends with the display of social cues; and in Group III, users experience the same design as Group II but without the display of social cues. We find that AI recommender systems lead to a higher content clickthrough rate and dwell time, whereas friend recommendations result in a larger content share rate and platform retention. The content difference is the main driver for the higher content clickthrough rate and dwell time of AI recommender systems and for the larger content share rate of friend recommendations. The presence of social cues (influence) contributes to the higher platform retention associated with friend recommendations. We further demonstrate that the performance differences between AI and friend recommendations vary across characteristics of users (user activeness) and content (homophily level and the number of displayed social cues). Our findings have rich implications for the mechanism and management of online content recommendations.