The Best Trail Algorithm for Assisted Navigation of Web Sites

TL;DR

The paper introduces the Best Trail Algorithm, an automated method for constructing relevant, compact navigation trails through web content, enhancing hypertext navigation by predicting future navigation potential.

Contribution

It presents a novel probabilistic best-first expansion algorithm, new scoring and filtering methods, and a metric called potential gain for improved web navigation.

Findings

Effective construction of navigation trails demonstrated

Potential gain metric predicts future navigation opportunities

Algorithm improves hypertext navigation efficiency

Abstract

We present an algorithm called the Best Trail Algorithm, which helps solve the hypertext navigation problem by automating the construction of memex-like trails through the corpus. The algorithm performs a probabilistic best-first expansion of a set of navigation trees to find relevant and compact trails. We describe the implementation of the algorithm, scoring methods for trails, filtering algorithms and a new metric called \emph{potential gain} which measures the potential of a page for future navigation opportunities.