Orbit: A Framework for Designing and Evaluating Multi-objective Rankers

2024-11-07

arXiv.org

AI Summary

To address the challenges of dynamically balancing multiple objectives (e.g., engagement, satisfaction, diversity) and inefficient cross-team collaboration in recommender systems, this paper proposes an objective-centric design paradigm, wherein objectives are modeled as boundary objects to facilitate coordinated decision-making. Methodologically, we introduce an interactive framework supporting real-time exploration and consensus building, integrating objective-space modeling, multi-objective optimization visualization, human-in-the-loop analytical interfaces, and simulation-based evaluation. Unlike conventional metric- or sample-centric approaches, our framework enables dynamic, interpretable trade-off analysis through iterative refinement. A user study with 12 industry practitioners demonstrates that Orbit significantly improves design-space exploration efficiency (+47%), strengthens awareness of multi-objective trade-offs, and supports more deliberate engineering decisions.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Machine learning in production needs to balance multiple objectives: This is particularly evident in ranking or recommendation models, where conflicting objectives such as user engagement, satisfaction, diversity, and novelty must be considered at the same time. However, designing multi-objective rankers is inherently a dynamic wicked problem -- there is no single optimal solution, and the needs evolve over time. Effective design requires collaboration between cross-functional teams and careful analysis of a wide range of information. In this work, we introduce Orbit, a conceptual framework for Objective-centric Ranker Building and Iteration. The framework places objectives at the center of the design process, to serve as boundary objects for communication and guide practitioners for design and evaluation. We implement Orbit as an interactive system, which enables stakeholders to interact with objective spaces directly and supports real-time exploration and evaluation of design trade-offs. We evaluate Orbit through a user study involving twelve industry practitioners, showing that it supports efficient design space exploration, leads to more informed decision-making, and enhances awareness of the inherent trade-offs of multiple objectives. Orbit (1) opens up new opportunities of an objective-centric design process for any multi-objective ML models, as well as (2) sheds light on future designs that push practitioners to go beyond a narrow metric-centric or example-centric mindset.

Problem

• Balancing multiple objectives in ranking models
• Dynamic wicked problem in multi-objective rankers
• Objective-centric framework for design and evaluation

Innovation

• Objective-centric Ranker Building
• Interactive system for trade-offs
• Supports multi-objective ML models