Using Groups of Items for Preference Elicitation in Recommender Systems. Chang, S., Harper, F M., & Terveen, L. In CSCW '15, pages 1258–1269, New York, NY, USA, 2015. ACM. Journal Abbreviation: CSCW '15![Using Groups of Items for Preference Elicitation in Recommender Systems [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 1 download To achieve high quality initial personalization, recommender systems must provide an efficient and effective process for new users to express their preferences. We propose that this goal is best served not by the classical method where users begin by expressing preferences for individual items - this process is an inefficient way to convert a user's effort into improved personalization. Rather, we propose that new users can begin by expressing their preferences for groups of items. We test this idea by designing and evaluating an interactive process where users express preferences across groups of items that are automatically generated by clustering algorithms. We contribute a strategy for recommending items based on these preferences that is generalizable to any collaborative filtering-based system. We evaluate our process with both offline simulation methods and an online user experiment. We find that, as compared with a baseline rate-15-items interface, (a) users are able to complete the preference elicitation process in less than half the time, and (b) users are more satisfied with the resulting recommended items. Our evaluation reveals several advantages and other trade-offs involved in moving from item-based preference elicitation to group-based preference elicitation.
@inproceedings{chang_using_2015,
address = {New York, NY, USA},
title = {Using {Groups} of {Items} for {Preference} {Elicitation} in {Recommender} {Systems}},
url = {http://doi.acm.org/10.1145/2675133.2675210},
doi = {10.1145/2675133.2675210},
abstract = {To achieve high quality initial personalization, recommender systems must
provide an efficient and effective process for new users to express their
preferences. We propose that this goal is best served not by the classical
method where users begin by expressing preferences for individual items -
this process is an inefficient way to convert a user's effort into
improved personalization. Rather, we propose that new users can begin by
expressing their preferences for groups of items. We test this idea by
designing and evaluating an interactive process where users express
preferences across groups of items that are automatically generated by
clustering algorithms. We contribute a strategy for recommending items
based on these preferences that is generalizable to any collaborative
filtering-based system. We evaluate our process with both offline
simulation methods and an online user experiment. We find that, as
compared with a baseline rate-15-items interface, (a) users are able to
complete the preference elicitation process in less than half the time,
and (b) users are more satisfied with the resulting recommended items. Our
evaluation reveals several advantages and other trade-offs involved in
moving from item-based preference elicitation to group-based preference
elicitation.},
urldate = {2015-09-19},
booktitle = {{CSCW} '15},
publisher = {ACM},
author = {Chang, Shuo and Harper, F Maxwell and Terveen, Loren},
year = {2015},
note = {Journal Abbreviation: CSCW '15},
pages = {1258--1269},
}
Downloads: 1
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