Real-world recommender systems often allow users to adjust the presented content through a variety of preference elicitation techniques such as \u201cliking\u201d or interest profiles. These elicitation techniques trade-off time and effort to users with the richness of the signal they provide to learning component driving the recommendations. In this paper, we explore this trade-off, seeking new ways for people to express their preferences with the goal of improving communication channels between users and the recommender system. Through a need-finding study, we observe the patterns in how people express their preferences during curation task, propose a taxonomy for organizing them, and point out research opportunities. We present a case study that illustrates how using this taxonomy to design an onboarding experience can lead to more accurate machine-learned recommendations while maintaining user satisfaction under low effort.<\/p>\n","protected":false},"excerpt":{"rendered":"
Real-world recommender systems often allow users to adjust the presented content through a variety of preference elicitation techniques such as \u201cliking\u201d or interest profiles. These elicitation techniques trade-off time and effort to users with the richness of the signal they provide to learning component driving the recommendations. In this paper, we explore this trade-off, seeking 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