Location
260-092, Owen G. Glenn Building
Start Date
12-15-2014
Description
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 effects of consumers’ perceived levels of recommendation novelty and serendipity on perceived preference fit and enjoyment. We find that merely increasing the level of novel recommendations is disadvantageous. Instead, recommenders should provide more serendipitous recommendations as this leads to higher perceived preference fit and enjoyment. In addition, market and recommender technology characteristics must be taken into account, since they partially determine the level of novel and serendipitous recommendations. Our findings have significant implications for research as they add additional insights on users’ evaluations of recommender systems. For practice, our results support online retailers in developing better recommenders.
Recommended Citation
Matt, Christian; Benlian, Alexander; Hess, Thomas; and Weiß, Christian, "Escaping from the Filter Bubble? The Effects of Novelty and Serendipity on Users’ Evaluations of Online Recommendations" (2014). ICIS 2014 Proceedings. 67.
https://aisel.aisnet.org/icis2014/proceedings/HumanBehavior/67
Escaping from the Filter Bubble? The Effects of Novelty and Serendipity on Users’ Evaluations of Online Recommendations
260-092, Owen G. Glenn Building
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 effects of consumers’ perceived levels of recommendation novelty and serendipity on perceived preference fit and enjoyment. We find that merely increasing the level of novel recommendations is disadvantageous. Instead, recommenders should provide more serendipitous recommendations as this leads to higher perceived preference fit and enjoyment. In addition, market and recommender technology characteristics must be taken into account, since they partially determine the level of novel and serendipitous recommendations. Our findings have significant implications for research as they add additional insights on users’ evaluations of recommender systems. For practice, our results support online retailers in developing better recommenders.