Preference Aggregation in Group Recommender Systems for Committee Decision-Making
Jacob Baskin, Shriram Krishnamurthi
ACM Conference on Recommender Systems, 2009
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.
You can find the long version, including details, as a technical report.
These papers may differ in formatting from the versions that appear in print. They are made available only to support the rapid dissemination of results; the printed versions, not these, should be considered definitive. The copyrights belong to their respective owners.