Improving Product Browsing whilst Engaging Users

We describe a new browsing system that improves online navigation of large numbers of objects which are otherwise difficult to label or split into categories. We use a two-stage process to obtain human similarity information which forms the core of the system and is used to create a resizable self-organizing map. This comprises a matrix of image stacks that provides a fast, intuitive and highly visual navigation environment. It can be easily implemented in sites, blogs and newsletters. We report the methods used to obtain the similarity data and derive the navigation system, and illustrate this with an example taken from the domain of abstract art

Browsing Abstract Art by Appearance

In this paper we describe an innovative browsing system that improves on the navigation of a large set of objects which are either difficult to label or categorize. We use a two-stage process to obtain similarity information from humans, which then becomes the core of the system, this perceptual information is then used to create a resizable self-organizing map which comprises a matrix of image stacks that provides a fast, intuitive and highly visual navigation environment. We report the methods used to obtain the similarity data from observer to derive the navigation system and illustrate this with an example from the abstract art domain. This record was migrated from the OpenDepot repository service in June, 2017 before shutting down