Query Completion Using Bandits for Engines Aggregation

TL;DR

This paper explores combining multiple query auto-completion engines using bandit algorithms to improve suggestion quality and diversity, demonstrating that mixtures outperform individual engines on real datasets.

Contribution

It introduces a bandit-based approach to optimally aggregate diverse query auto-completion engines, enhancing recommendation performance.

Findings

Mixtures of engines outperform single engines in experiments.

Bandit strategies effectively optimize engine aggregation.

Aggregation improves diversity and relevance of suggestions.

Abstract

Assisting users by suggesting completed queries as they type is a common feature of search systems known as query auto-completion. A query auto-completion engine may use prior signals and available information (e.g., user is anonymous, user has a history, user visited the site before the search or not, etc.) in order to improve its recommendations. There are many possible strategies for query auto-completion and a challenge is to design one optimal engine that considers and uses all available information. When different strategies are used to produce the suggestions, it becomes hard to rank these heterogeneous suggestions. An alternative strategy could be to aggregate several engines in order to enhance the diversity of recommendations by combining the capacity of each engine to digest available information differently, while keeping the simplicity of each engine. The main objective of this research is therefore to find such mixture of query completion engines that would beat any engine taken alone. We tackle this problem under the bandits setting and evaluate four strategies to overcome this challenge. Experiments conducted on three real datasets show that a mixture of engines can outperform a single engine.