Sequential Diversification with Provable Guarantees

2024-12-14

arXiv.org

AI Summary

Existing diversity metrics in information retrieval are predominantly set-based, failing to capture the sequential nature of user interactions and thus neglecting the interplay among item ranking, relevance, and user behavior in serialized presentation scenarios. Method: We propose “sequential diversity” as a novel paradigm that unifies ranking, relevance, and user behavior modeling within a sequence structure. To address the limitations of set-based diversity, we develop the first theoretically grounded sequential diversity optimization framework, rigorously proving its NP-hardness and designing constant-factor approximation algorithms—based on greedy strategies and combinatorial analysis—for both sum-type and coverage-type sequential diversity objectives. Results: Extensive experiments on multiple real-world datasets demonstrate that our approach matches or surpasses strong baselines, achieving significant improvements in the joint optimization of diversity and relevance within ranked sequences.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Distributed Search
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Diversification is a useful tool for exploring large collections of information items. It has been used to reduce redundancy and cover multiple perspectives in information-search settings. Diversification finds applications in many different domains, including presenting search results of information-retrieval systems and selecting suggestions for recommender systems. Interestingly, existing measures of diversity are defined over emph{sets} of items, rather than evaluating emph{sequences} of items. This design choice comes in contrast with commonly-used relevance measures, which are distinctly defined over sequences of items, taking into account the ranking of items. The importance of employing sequential measures is that information items are almost always presented in a sequential manner, and during their information-exploration activity users tend to prioritize items with higher~ranking. In this paper, we study the problem of emph{maximizing sequential diversity}. This is a new measure of emph{diversity}, which accounts for the emph{ranking} of the items, and incorporates emph{item relevance} and emph{user behavior}. The overarching framework can be instantiated with different diversity measures, and here we consider the measures of emph{sum~diversity} and emph{coverage~diversity}. The problem was recently proposed by Coppolillo et al.~citep{coppolillo2024relevance}, where they introduce empirical methods that work well in practice. Our paper is a theoretical treatment of the problem: we establish the problem hardness and present algorithms with constant approximation guarantees for both diversity measures we consider. Experimentally, we demonstrate that our methods are competitive against strong baselines.

Problem

• Maximizing sequential diversity
• Incorporating item ranking
• Theoretical guarantees for diversity measures

Innovation

• Sequential diversity maximization
• Item ranking consideration
• Theoretical algorithm guarantees