Dynamic Linking of Smart Digital Objects Based on User Navigation Patterns

TL;DR

This paper presents a method for dynamically linking digital objects by analyzing user navigation patterns with an unsupervised learning approach, enabling adaptive and community-driven link structures.

Contribution

It introduces an unsupervised learning mechanism to automatically generate and adjust links among digital objects based on user behavior, enhancing navigation relevance.

Findings

Buckets adapt links based on user selections

Generated networks reflect collective user preferences

Adaptive linking improves navigation experience

Abstract

We discuss a methodology to dynamically generate links among digital objects by means of an unsupervised learning mechanism which analyzes user link traversal patterns. We performed an experiment with a test bed of 150 complex data objects, referred to as buckets. Each bucket manages its own content, provides methods to interact with users and individually maintains a set of links to other buckets. We demonstrate that buckets were capable of dynamically adjusting their links to other buckets according to user link selections, thereby generating a meaningful network of bucket relations. Our results indicate such adaptive networks of linked buckets approximate the collective link preferences of a community of user