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

Recommender systems aim to support consumers 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 users. On the other hand, providing unconventional items
increases 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 insufficient. Instead,
recommenders should provide more serendipitous recommendations as
this leads to higher perceived preference fit and enjoyment. In addition,
market and recommender technology characteristics need to be taken into
account as 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 enable online retailers to develop better
recommenders.