Lessons on Applying Automated Recommender Systems to Information-Seeking Tasks
Abstract
Abstract * Automated recommender systems predict user preferences by applying machine learning techniques to data on products, users, and past user preferences for products. Such systems have become increasingly popular in entertainment and e-commerce domains, but have thus far had little success in information-seeking domains such as identifying published research of interest. We report on several recent publications that show how recommenders can be extended to more effectively address informationseeking tasks by expanding the focus from accurate prediction of user preferences to identifying a useful set of items to recommend in response to the user's specific information need. Specific research demonstrates the value of diversity in recommendation lists, shows how users value
Cite
Text
Konstan et al. "Lessons on Applying Automated Recommender Systems to Information-Seeking Tasks." AAAI Conference on Artificial Intelligence, 2006.Markdown
[Konstan et al. "Lessons on Applying Automated Recommender Systems to Information-Seeking Tasks." AAAI Conference on Artificial Intelligence, 2006.](https://mlanthology.org/aaai/2006/konstan2006aaai-lessons/)BibTeX
@inproceedings{konstan2006aaai-lessons,
title = {{Lessons on Applying Automated Recommender Systems to Information-Seeking Tasks}},
author = {Konstan, Joseph A. and McNee, Sean M. and Ziegler, Cai-Nicolas and Torres, Roberto and Kapoor, Nishikant and Riedl, John},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2006},
pages = {1630-1633},
url = {https://mlanthology.org/aaai/2006/konstan2006aaai-lessons/}
}