Shuffling a Stacked Deck: The Case for Partially Randomized Ranking of Search Engine Results

TL;DR

Introducing a controlled amount of randomness into search rankings can help new, high-quality pages gain visibility, balancing exploration of new content with exploitation of established popular pages.

Contribution

This paper proposes and analyzes a simple method of adding partial randomness to search rankings to improve the visibility of new, high-quality pages, addressing a key limitation of current ranking systems.

Findings

A modest amount of randomness improves search result quality.

Tradeoff exists between exploration of new pages and exploitation of popular ones.

Simulation and analytical models demonstrate benefits of partial randomness.

Abstract

In-degree, PageRank, number of visits and other measures of Web page popularity significantly influence the ranking of search results by modern search engines. The assumption is that popularity is closely correlated with quality, a more elusive concept that is difficult to measure directly. Unfortunately, the correlation between popularity and quality is very weak for newly-created pages that have yet to receive many visits and/or in-links. Worse, since discovery of new content is largely done by querying search engines, and because users usually focus their attention on the top few results, newly-created but high-quality pages are effectively ``shut out,'' and it can take a very long time before they become popular. We propose a simple and elegant solution to this problem: the introduction of a controlled amount of randomness into search result ranking methods. Doing so offers new pages a chance to prove their worth, although clearly using too much randomness will degrade result quality and annul any benefits achieved. Hence there is a tradeoff between exploration to estimate the quality of new pages and exploitation of pages already known to be of high quality. We study this tradeoff both analytically and via simulation, in the context of an economic objective function based on aggregate result quality amortized over time. We show that a modest amount of randomness leads to improved search results.