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  1. ; ; ; ( , Proceedings of the Second Workshop on Engineering Computer-Human Interaction in Recommender Systems co-located with the 9th ACM SIGCHI Symposium on Engineering Interactive Computing Systems)
    Traditionally, recommender systems were built with the goal of aiding users’ decision-making process by extrapolating what they like and what they have done to predict what they want next. However, in attempting to personalize the suggestions to users’ preferences, these systems create an isolated universe of information for each user, which may limit their perspectives and promote complacency. In this paper, we describe our research plan to test a novel approach to recommender systems that goes beyond “good recommendations” that supports user aspirations and exploration. 
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    Accepted Manuscript Full Text Available
  2. ; ; ( , Proceedings of the 10th ACM Conference on Recommender Systems)
    Every day, we are confronted with an abundance of decisions that require us to choose from a seemingly endless number of choice options. Recommender systems are supposed to help us deal with this formidable task, but some scholars claim that these systems instead put us inside a "Filter Bubble" that severely limits our perspectives. This paper presents a new direction for recommender systems research with the main goal of supporting users in developing, exploring, and understanding their unique personal preferences. 
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    Full Text Available