Recommender systems help users find items of interest and help websites and marketers select items to promote. Today's recommender systems incorporate sophisticated technology to model user preferences, model item properties, and leverage the experiences of a large community of users in the service of better recommendations. Yet all too often better recommendations--at least by traditional measures of accuracy and precision--fail to meet the goal of improving user experience. This talk will take a look at successes and failures in moving beyond basic machine learning approaches to recommender systems to emphasize factors tied to user behavior and experience. Along the way, we will explore a generalizable approach to combining human-centered evaluation with data mining and machine learning techniques. [Go to the full record in the library's catalogue]
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