“I thought it was terrible and everyone else loved it” — A New Perspective for Effective Recommender System Design

@incollection{citeulike:2831409, 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.}, author = {Bonhard, Philip and Sasse, Angela M. }, citeulike-article-id = {2831409}, doi = {10.1007/1-84628-249-7\_16}, journal = {People and Computers XIX — The Bigger Picture}, keywords = {recommendation, topic1}, pages = {251--265}, posted-at = {2008-05-25 20:31:37}, priority = {2}, title = {“I thought it was terrible and everyone else loved it” — A New Perspective for Effective Recommender System Design}, url = {http://dx.doi.org/10.1007/1-84628-249-7\_16}, year = {2006} }

TY - CHAP ID - citeulike:2831409 L3 - citeulike-article-id:2831409 TI - “I thought it was terrible and everyone else loved it” — A New Perspective for Effective Recommender System Design JF - People and Computers XIX — The Bigger Picture SP - 251 EP - 265 N2 - 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. KW - recommendation KW - topic1 AU - Bonhard, Philip AU - Sasse, Angela PY - 2006/// UR - http://dx.doi.org/10.1007/1-84628-249-7_16 ER -