ABSTRACT
Recommender systems form the backbone of modern e-commerce, suggesting items to users based on the collection of algorithmic data of a user's preferences. Companies that use recommender systems claim that they can give users what they want, or more precisely, what they desire. Netflix, for example, gives users recommended movies based on the user's behaviour on the platform, thereby listing new movies that the user may want to watch. This article explores whether there is a difference between what engages us, on the one hand, and what we truly want to want, on the other. This builds on the hierarchical structure of desires, as posed by Harry Frankfurt and Gerald Dworkin. Recommender systems, to use Frankfurt's terminology, may not allow for the formation of second-order desires, or for users to consider what they want to want. Indeed, recommender systems may rely on a narrow form of human engagement, a voyeuristic mode, rather than an active wanting. In bypassing second-order desires, there is a risk that recommender systems can start to control the user, rather than the user controlling the algorithm. This raises important questions concerning human autonomy, trustworthiness, and Byung-Chul Han's conception of an information regime, where the owners of the data make decisions about what users consume online, and ultimately, how they live their lives.
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Index Terms
- Recommender Systems, Autonomy and User Engagement
Recommendations
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...
User Personality and User Satisfaction with Recommender Systems
In this study, we show that individual users' preferences for the level of diversity, popularity, and serendipity in recommendation lists cannot be inferred from their ratings alone. We demonstrate that we can extract strong signals about individual ...
Investigating serendipity in recommender systems based on real user feedback
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied ComputingOver the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-...
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