This document summarizes a study on a new recommendation algorithm called K-Furthest Neighbor (KFN) which recommends items that are disliked by users dissimilar to the target user. The study found that: 1) KFN performed worse than the standard K-Nearest Neighbor algorithm in offline evaluation metrics but was perceived as more useful by users in online evaluations. 2) Users found the recommendations from KFN to be less obvious and recognizable but similarly serendipitous and useful as the standard algorithm. 3) Recommending items disliked by dissimilar users leads to more diverse recommendations while maintaining comparable overall usefulness, even if standard offline metrics say otherwise.