  Recommender systems influence almost every aspect of our digital lives.
Unfortunately, in striving to give us what we want, they end up restricting our
open-mindedness. Current recommender systems promote echo chambers, where
people only see the information they want to see, and homophily, where users of
similar background see similar content. We propose a new serendipity metric to
measure the presence of echo chambers and homophily in recommendation systems
using cluster analysis. We then attempt to improve the diversity-preservation
qualities of well known recommendation techniques by adopting a parent
selection algorithm from the evolutionary computation literature known as
lexicase selection. Our results show that lexicase selection, or a mixture of
lexicase selection and ranking, outperforms its purely ranked counterparts in
terms of personalization, coverage and our specifically designed serendipity
benchmark, while only slightly under-performing in terms of accuracy (hit
rate). We verify these results across a variety of recommendation list sizes.
In this work we show that lexicase selection is able to maintain multiple
diverse clusters of item recommendations that are each relevant for the
specific user, while still maintaining a high hit-rate accuracy, a trade off
that is not achieved by other methods.
