Escaping from the Filter Bubble? The Effects of Novelty and Serendipity on Users' Evaluations of Online Recommendations

Abstract

Recommender systems aim to support users in identifying the most relevant items. However, there are concerns that recommenders may imprison users in a “filter bubble” by recommending items predominantly known to them. On the other hand, providing unconventional items may increase risks of not meeting users’ taste. Given this trade-off, we analyze the… (More)

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