[Paper Review] Adapting to the Shifting Intent of Search Queries

Syed Umar, Aleksandrs Slivkins+1 more|arXiv (Cornell University)|Jul 22, 2010

Advanced Bandit Algorithms Research21 references3 citations

TL;DR

This paper proposes a meta-algorithm, bwc, that combines online classification with bandit learning to detect and adapt to abrupt shifts in user search intent. By using contextual signals to trigger bandit restarts when intent shifts are detected, the method achieves regret logarithmic in the number of impressions, significantly outperforming prior approaches under high intent-shifting traffic.

ABSTRACT

Search engines today present results that are often oblivious to abrupt shifts in intent. For example, the query `independence day' usually refers to a US holiday, but the intent of this query abruptly changed during the release of a major film by that name. While no studies exactly quantify the magnitude of intent-shifting traffic, studies suggest that news events, seasonal topics, pop culture, etc account for 50% of all search queries. This paper shows that the signals a search engine receives can be used to both determine that a shift in intent has happened, as well as find a result that is now more relevant. We present a meta-algorithm that marries a classifier with a bandit algorithm to achieve regret that depends logarithmically on the number of query impressions, under certain assumptions. We provide strong evidence that this regret is close to the best achievable. Finally, via a series of experiments, we demonstrate that our algorithm outperforms prior approaches, particularly as the amount of intent-shifting traffic increases.

Motivation & Objective

• Address the challenge of search engines failing to adapt to sudden shifts in user intent, such as those caused by breaking news or viral content.
• Develop a method that leverages unlabeled contextual signals and real-time click feedback to detect intent shifts without requiring labeled training data.
• Design a system that dynamically restarts bandit learning upon detecting intent shifts to quickly converge on more relevant results.
• Provide theoretical guarantees on regret that scale logarithmically with the number of impressions, under mild assumptions on the classifier and bandit components.

Proposed method

• Introduce a meta-algorithm, bwc, that alternates between 'testing' and 'adapting' phases to detect intent shifts using contextual features.
• Use a classifier to predict whether an intent shift has occurred based on query context (e.g., volume, news mentions, document age), with no labeled data required.
• Leverage bandit algorithms (specifically a modified UCB1 variant) to select and learn from click feedback, restarting when the classifier predicts a shift.
• Generate synthetic labels for the classifier using a consistency check: if the set of high-confidence arms before and after a phase are disjoint, label as 'false' (no event), otherwise 'true'.
• Ensure reliable feedback by requiring full phases (of length L) before and after a potential event to avoid contamination from transient behavior.
• Formalize the system using a modular framework where bandit regret and classifier FP-complexity are separately bounded and combined into a total regret bound.

Experimental results

Research questions

• RQ1 Can a search system detect abrupt shifts in user intent using only unlabeled contextual signals and click feedback?
• RQ2 Is it possible to achieve regret that scales logarithmically with the number of impressions in the presence of intent shifts?
• RQ3 How does the integration of a classifier with a bandit algorithm improve adaptation speed compared to standard bandit methods?
• RQ4 What is the theoretical limit on regret in such a setting, and can the proposed method achieve near-optimality?
• RQ5 How does the performance of the meta-algorithm depend on the complexity of the concept class used by the classifier and the number of events?

Key findings

• The bwc meta-algorithm achieves a regret bound of $ O((k + d_{ ext{FP}})(n/ abla) frac{1}{ abla} ext{log }T) $, which is logarithmic in $ T $, under mild assumptions, indicating strong adaptability to intent shifts.
• The regret bound depends on the number of events $ k $, the FP-complexity $ d_{ ext{FP}} $ of the classifier, and the minimum suboptimality $ abla $, showing that both classifier accuracy and event frequency affect performance.
• A lower bound is established showing that any context-ignoring bandit algorithm incurs $ ilde{ abla}( abla{T}) $ regret, proving that context is essential for achieving logarithmic regret.
• The method is shown to be nearly optimal in terms of dependence on $ k $ and $ d_{ ext{FP}} $, assuming logarithmic regret is achievable.
• Synthetic experiments demonstrate that bwc outperforms standard bandit algorithms like UCB1, especially as intent-shifting traffic increases.
• The bandit subroutine with high-confidence suboptimality estimates is shown to be of independent interest and contributes to the algorithm’s robustness and theoretical guarantees.