Ranking with Multiple Objectives
Nikhil R. Devanur, Sivakanth Gopi

TL;DR
This paper introduces a framework for ranking that balances multiple objectives by maximizing a concave function of weighted score aggregates, providing an approximation algorithm with resource augmentation guarantees, validated on synthetic and real data.
Contribution
It formulates a general multi-objective ranking problem and proposes an approximation algorithm with resource augmentation guarantees for balanced outcomes.
Findings
The algorithm achieves near-optimal results with slight resource augmentation.
Simulations demonstrate effective balancing of objectives on synthetic and real data.
The approach generalizes to various aggregation functions and ranking scenarios.
Abstract
In search and advertisement ranking, it is often required to simultaneously maximize multiple objectives. For example, the objectives can correspond to multiple intents of a search query, or in the context of advertising, they can be relevance and revenue. It is important to efficiently find rankings which strike a good balance between such objectives. Motivated by such applications, we formulate a general class of problems where - each result gets a different score corresponding to each objective, - the results of a ranking are aggregated by taking, for each objective, a weighted sum of the scores in the order of the ranking, and - an arbitrary concave function of the aggregates is maximized. Combining the aggregates using a concave function will naturally lead to more balanced outcomes. We give an approximation algorithm in a bicriteria/resource augmentation setting: the…
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Taxonomy
TopicsMulti-Criteria Decision Making · Game Theory and Voting Systems · Data Management and Algorithms
