Hey Alexa, What Should I Read? Comparing the Use of Social and Algorithmic Recommendations for Different Reading Genres

Abstract

Users often seek reading recommendations for what to read, across a variety of topics of interest and genres. While there has been extensive research on the development of recommender algorithms, our understanding of social factors relating to reading recommendation in the digital era is poor. We have no holistic view of how readers interact with diverse resources, social and digital, to obtain reading recommendations. Users can consult computer-generated summaries and human-created reviews. How much or how often the typical user relies on one or other source, or what variations there are by genre of intended reading, are both open questions. To narrow these research gaps, we conducted a diary study to capture a comprehensive picture of readers’ use of algorithm- and social-sourced information to inform their future reading choices. Based on a qualitative analysis of these diaries, we produced a survey to investigate in-depth readers’ recommendation preferences across fictional reading, factual reading, academic resources, and news and articles. We show that users rely on different sources of recommendation information in different ways across different genres, and that modern social media plays an increasing role alongside established mass media, especially for fiction.