Parallel Exploration for Product Catalogs
What Is Parallel Exploration?
Almost all on-line commercial product catalogs are based on the paradigm of faceted search/browsing: You enter keywords and apply filters to zoom in on sets of products that are of interest to you.
This paradigm has a major limitation: You can view only one set of results at a time; if you don’t like the current set, you have to backtrack and try to find a more interesting set.
Parallel Exploration extends faceted search/browsing to overcome this limitation: All of the result sets that you have viewed are stored in an “exploration tree”. So you can easily go back to previous result sets and view multiple result sets at once.
This ability enables you to explore a product catalog in several directions at once when you’re not sure what the ideal product will be like. You can also look for two or more related products in parallel.
How Has Parallel Exploration Been Applied So Far?
Parallel Exploration was developed over several years at the German Research Center for Artificial Intelligence (DFKI) in the group of Chusable founder Anthony Jameson. In the domain of city events and places, it played a key role in the innovation activity 3cixty, funded by EIT Digital, which won the 2015 Semantic Web Challenge. At the end of 2016, DFKI transferred the intellectual rights to Parallel Exploration to Chusable so that it could be commercially exploited.
How Will Parallel Exploration Be Deployed by Chusable?
A number of important on-line vendors make it possible for affiliates to design alternative websites and applications to access their product catalogs. As a first step of exploitation, Chusable is developing and testing a website based on Parallel Exploration as an affiliate of one of these vendors. The website will be available to web visitors in the third or fourth quarter of 2017.
The Problem: Fragmented Knowledge That Designers Can’t Make Use Of
A great many interactive applications—ranging from complex decision support systems to small parts of mobile applications—have the function of helping people to make better choices in some particular domain. Anyone who designs (or evaluates) an application of this type should make use of existing knowledge about (a) how people make choices and (b) how computing technology can help people make better choices. There exists a vast amount of knowledge about these questions, but it is difficult for designers to make good use of it: The knowledge is spread over many lines of research in psychology, computer science, and other fields, which use different conceptual frameworks and adopt complementary perspectives.
How Does Chusapedia Solve This Problem?
Chusapedia provides two main types of functionality:
- A convenient platform for integrating within a coherent conceptual framework a wide range of knowledge about human choice and ways of supporting it.
- Interactive functionality for actively using this knowledge to analyize choices and to design ways of supporting them.
The foundation of Chusapedia is a collaboratively constructed semantic wiki (built on the same basic platform as Wikipedia) whose pages describe in a coherent way knowledge from a wide variety of sources relevant to the support of human choices. The semantic links in the wiki enable functionality that makes it possible for designers to use the integrated knowledge actively and systematically when designing or evaluating a choice-supporting application–and also to see how Chusapedia’s knowledge base is related to models and concepts that the designer is already familiar with.