@inproceedings{schnabel2020who,
author = {Schnabel, Tobias and Ramos, Gonzalo and Amershi, Saleema},
title = {“Who doesn’t like dinosaurs?” Finding and Eliciting Richer Preferences for Recommendation},
organization = {ACM},
booktitle = {14th ACM Conference on Recommender Systems},
year = {2020},
month = {September},
abstract = {Real-world recommender systems often allow users to adjust the presented content through a variety of preference elicitation techniques such as “liking” 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.},
publisher = {ACM},
url = {http://approjects.co.za/?big=en-us/research/publication/who-doesnt-like-dinosaurs-finding-and-eliciting-richer-preferences-for-recommendation/},
pages = {398-407},
}