Preference aggregation in group recommender systems for committee decision-making

Authors:
Jacob P. Baskin;Shriram Krishnamurthi
Affiliations:
Google, Inc., New York, NY, USA;Brown University, Providence, RI, USA
Venue:
Proceedings of the third ACM conference on Recommender systems
Year:
2009

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Abstract

We present a preference aggregation algorithm designed for situations in which a limited number of users each review a small subset of a large (but finite) set of candidates. This algorithm aggregates scores by using users' relative preferences to search for a Kemeny-optimal ordering of items, and then uses this ordering to identify good and bad items, as well as those that are the subject of reviewer conflict. The algorithm uses variable-neighborhood local search, allowing the efficient discovery of high-quality consensus orderings while remaining computationally feasible. It provides a significant increase in solution quality over existing systems. We discuss potential applications of this algorithm in group recommender systems for a variety of scenarios, including program committees and faculty searches.