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.

翻译：推荐系统几乎影响着我们数字生活的方方面面。不幸的是，在努力提供我们想要的内容时，它们最终限制了我们的开放性。当前的推荐系统助推了信息茧房（人们只看到自己想看的信息）和同质性（相似背景的用户看到相似内容）现象。我们提出了一种新的意外惊喜性指标，通过聚类分析测量推荐系统中的信息茧房和同质性。随后，我们尝试采用演化计算文献中一种名为lexicase选择的父代选择算法，来增强知名推荐技术的多样性保持特性。结果表明，在个性化、覆盖率以及我们专门设计的意外惊喜性基准测试中，lexicase选择或其与排序的混合方法均优于纯排序方法，仅在准确率（命中率）上略有不足。我们在多种推荐列表规模下验证了这些结果。本研究表明，lexicase选择能够维护多个多样化的物品推荐聚类，这些聚类均针对特定用户具有相关性，同时仍保持高命中率精度——这一权衡是其他方法未能实现的。