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Scheduled for release in 2017: Parallel Exploration and Chusapedia

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 hto leave the result set to try a different query.

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.

The exploration tree enables you to explore in several directions in parallel, “gathering” promising items from different result sets.

You can ask very specific queries, such as “find some products similar to this one” without losing track of what you’ve found so far.

At any time, you can view the gathered products in a separate “Compare” panel, sorting and zooming in on them to find the best ones.

Until you’re satisfied with one or more products, you can go back to the exploration tree to continue gathering new ideas.

These processes of “gathering” and “comparing” are a natural and effective part of everyday choosing – but they are not well supported by conventional product search interfaces.

How Has Parallel Exploration Been Applied So Far?


Parallel Exploration

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 property rights to Parallel Exploration to Chusable so that it could be commercially exploited.

How Is Parallel Exploration Being 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 has been developing and testing a website based on Parallel Exploration as an affiliate of one of these vendors. The website will be available to web toward the end of 2017.

Chusapedia: Analysing and Designing Choice-Supporting Interventions

Examples of choice-supporting interventions are:

  • a website for helping customers choose products on-line
  • a small part of a mobile application that helps users choose a method to perform a particular task
  • a multi-month, multi-person behavior change program to encourage people to eat healthier food

More generally, a choice-supporting intervention is any set of measures taken to help people make choices in a particular context.

Analysing an existing intervention – or designing a new or improved one – requires the application of a wide range of knowledge about how people make choices and how they can be helped to make better choices.

This knowledge is normally available only in the heads of experienced practitioners and in a wide range of publications from research in psychology, computer science, and other fields.

The relevant experts tend to use different conceptual frameworks and to adopt complementary perspectives.

Chusapedia is a web-based tool that makes it possible to access a wide range of relevant knowledge and to apply it effectively.

Chusapedia has two main components:

1. A Web Application for Analysing and Designing Interventions

The application helps the user to answer the following questions in a systematic way while making use of relevant general and specific knowledge:

  • Who are the choosers whose choices are being supported?
  • What choices of each chooser need to be supported?
  • What are likely steps that the choosers will take when making each choice – with or without an intervention?
  • What known strategies and tactics can be applied to support these choice steps?
  • What features of the intervention help to realize these tactics?

Answering these questions with the Chusapedia App helps to understand the gaps in any existing intervention and to generate well-founded ideas for a better intervention.

2.A Modular, Collaboratively Developed Knowledge Base With a Wide Range of Knowledge About Choice and Choice Support

The Chusapedia App continually accesses the Chusapedia knowledge base.

The knowledge base currently represents the ASPECT and ARCADE models from the book Choice Architecture for Human-Computer Interaction. During the last quarter of 2017, extensions of these models are being added that deal in more detail with

  • choices in particular domains (e.g., health and well-being)
  • particular types of choice problem (e.g., configuration problems)
  • ways of using particular technologies (e.g., recommender systems) to support choice

It will soon be possible for site visitors to add model extensions themselves to use in their own analyses and/or to benefit other Chusapedia users.

By the end of the first quarter of 2018, it will also be possible to introduce models based on different conceptual frameworks (e.g., from the persuasive technology or behavior change fields). So people accustomed to using these conceptual frameworks will be able to benefit from the functionality of the Chusapedia App.

Availability

Chusapedia will remain freely available for use in both practice and research.

A concrete example of the use of a pre-release version of the app can be found in the annotated slides from a recent keynote talk.

Those who would like to experiment with the most recent pre-release version themselves can access it here:

http://chusapedia-app.chusable.com

Starting in late 2017, Chusable will be offering the service of helping clients apply Chusapedia efficiently to improve their own choice-supporting interventions. Part of this work can include the creation of extensions of the knowledge base that are especially relevant to the client.