"I thought it was terrible and everyone else loved it" - A new perspective for effective recommender system design

Abstract

Recommender Systems have been developed to help people make choices, for instance when deciding what books to buy or movies to see. Research to date has focused on developing algorithms to improve the predictive accuracy of recommender systems. This paper presents an HCI approach to recommender systems design, based on the strategies people employ when seeking advice in taste domains from various sources. The results from a qualitative study with 44 participants show that participants have different requirements for different choice domains. In taste domains, the relationship between the advice seeker and recommender is extremely important, so ways of indicating social closeness and taste overlap are required. Recommender systems must establish a connection between the advice seeker and recommenders through explanation interfaces and communication functions.