%0 Conference Paper %1 Schnabel_2016 %A Schnabel, Tobias %A Bennett, Paul N. %A Dumais, Susan T. %A Joachims, Thorsten %B Proceedings of the 25th International Conference on World Wide Web %D 2016 %I International World Wide Web Conferences Steering Committee %K interactive-recommender www2006 %P 987-997 %R 10.1145/2872427.2883012 %T Using Shortlists to Support Decision Making and Improve Recommender System Performance %U https://doi.org/10.1145%2F2872427.2883012 %X In this paper, we study shortlists as an interface component for recommender systems with the dual goal of supporting the user's decision process, as well as improving implicit feedback elicitation for increased recommendation quality. A shortlist is a temporary list of candidates that the user is currently considering, e.g., a list of a few movies the user is currently considering for viewing. From a cognitive perspective, shortlists serve as digital short-term memory where users can off-load the items under consideration -- thereby decreasing their cognitive load. From a machine learning perspective, adding items to the shortlist generates a new implicit feedback signal as a by-product of exploration and decision making which can improve recommendation quality. Shortlisting therefore provides additional data for training recommendation systems without the increases in cognitive load that requesting explicit feedback would incur. We perform an user study with a movie recommendation setup to compare interfaces that offer shortlist support with those that do not. From the user studies we conclude: (i) users make better decisions with a shortlist; (ii) users prefer an interface with shortlist support; and (iii) the additional implicit feedback from sessions with a shortlist improves the quality of recommendations by nearly a factor of two.

@inproceedings{Schnabel_2016, abstract = {In this paper, we study shortlists as an interface component for recommender systems with the dual goal of supporting the user's decision process, as well as improving implicit feedback elicitation for increased recommendation quality. A shortlist is a temporary list of candidates that the user is currently considering, e.g., a list of a few movies the user is currently considering for viewing. From a cognitive perspective, shortlists serve as digital short-term memory where users can off-load the items under consideration -- thereby decreasing their cognitive load. From a machine learning perspective, adding items to the shortlist generates a new implicit feedback signal as a by-product of exploration and decision making which can improve recommendation quality. Shortlisting therefore provides additional data for training recommendation systems without the increases in cognitive load that requesting explicit feedback would incur. We perform an user study with a movie recommendation setup to compare interfaces that offer shortlist support with those that do not. From the user studies we conclude: (i) users make better decisions with a shortlist; (ii) users prefer an interface with shortlist support; and (iii) the additional implicit feedback from sessions with a shortlist improves the quality of recommendations by nearly a factor of two.}, added-at = {2022-11-11T21:16:30.000+0100}, author = {Schnabel, Tobias and Bennett, Paul N. and Dumais, Susan T. and Joachims, Thorsten}, biburl = {https://www.bibsonomy.org/bibtex/23ac60c1f60798a9cc1cfe3782e62f2f2/brusilovsky}, booktitle = {Proceedings of the 25th International Conference on World Wide Web}, description = {Using Shortlists to Support Decision Making and Improve Recommender System Performance | Proceedings of the 25th International Conference on World Wide Web}, doi = {10.1145/2872427.2883012}, interhash = {5ab5cbeceb98f444e9189d14cc508039}, intrahash = {3ac60c1f60798a9cc1cfe3782e62f2f2}, keywords = {interactive-recommender www2006}, month = apr, pages = {987-997}, publisher = {International World Wide Web Conferences Steering Committee}, timestamp = {2022-11-11T21:16:30.000+0100}, title = {Using Shortlists to Support Decision Making and Improve Recommender System Performance}, url = {https://doi.org/10.1145%2F2872427.2883012}, year = 2016 }