Managing the large volumes of data generated by the Social Web presents many challenges. In considering the user experience of NoTube’s Beancounter we have been thinking about how to present this kind of data to users in meaningful ways, as well as ensuring we implement robust models of sharing, privacy and ownership.

The importance of getting these things right is reflected in the growing interest outside of the NoTube project in data mining of activity and social data for the provision of social recommendations.

To re-cap, NoTube’s Beancounter technology supports the automatic generation of an implicit user interests profile. This is based on re-use of an individual’s activity on social media services, such as the content of their tweets and Facebook ‘likes’, to determine their interests. The idea is to re-use the scattered and disparate activity data and make it useful by combining it, looking for patterns, and using it to suggest things to watch.

Beancounter is currently being re-built in the backend. Alongside this we have been working with visual designers from the design agency Fabrique in Amsterdam to develop a new user interface for the Web front-end. This blog post outlines some of the design challenges we have encountered along the way.

Challenge 1: Displaying an overview of your interests

To create your Beancounter profile you need to link one or more social media accounts (Facebook, Twitter, LastFM etc) to your Beancounter account. Beancounter retrieves activity data from these sources, interprets the information contained in the activities and matches it to concepts from the Linked Open Data cloud (DBpedia concepts) to give you an overview of your interests. These interests include programmes, movies, people, locations and genres. Each interest is assigned a weight in the profile; the more instances of an interest in your activities, the higher the weight of the interest - and the more influence it has on your recommendations. These weightings change over time as the Beancounter continually adjusts to your activities. For example, if you watch or listen to lots of coverage of The Olympic Games but aren’t generally interested in sport the rest of the time, the weightings for sport-related interests will temporarily increase during The Olympics.

This interests profile is useful because it can be automatically used as input for personalised TV recommendations (via NoTube’s N-Screen prototype for example) to help you decide what to watch. And because the profile is portable, it could also be used as input for other applications.

During implementation of the first version of the Beancounter it became clear that there are potentially a very large number of interests to display for any given user, many of which have very low weightings because they have only appeared once or twice in the user’s activities and therefore have very little significance for recommendations.

We wanted a simple way to clearly present the spread of interests, which also immediately conveys the influence (i.e. weight) of each interest for recommendations, and takes account of the fact that the number of interests could quickly become quite large. As a solution, the designers adapted the tag cloud metaphor into a scaleable grid, with variable font and cell sizes to indicate relative weightings and hierarchy, so that the most influential concepts are prioritised at the top.

Challenge 2: Showing how each activity affects your interests

Another of the UI design challenges that emerged from implementation of the first version of the Beancounter is how to show the contribution of an activity to the ‘evolution’ of your interests. For example, as described in a video of an earlier demo, clicking on the activity “You watched Timecop…” in the user’s activity stream opens a pop-up (shown in the screenshot below) using bar charts to show the ‘before’ and ‘after’ status of the user’s interests based on watching the film Timecop. The two existing concepts ‘1990s science fiction films’ and ‘Films shot anamorphically’ now have a greater weighting than they did before. The other concepts (where only the blue bar is displayed) are new interests associated with Timecop, which didn’t already exist in the profile.

We wanted to simplify the presentation of this information, and to make it easier to understand what is happening and why it might be interesting. We’re getting there with the new design, although we’re still thinking about the best way to convey the idea that the length of the bar really represents ‘the influence of this concept on your recommendations’, and of providing a relative scale to measure this level of influence against.

This design also allows the possibility for the functionality to be extended so that you could manually adjust the weighting of a particular concept (for example, by sliding the bar to the left or the right) to give it more or less influence over recommendations.

Challenge 3: Displaying on-the-fly data analytics

In addition to displaying your interests, it has always been our intention to offer some analysis of the data that the Beancounter has collected about you. This is based on the premise that people are usually interested in information about themselves, and the initial inspiration came from the Dopplr annual report and the BBC’s RadioPop prototype for social radio listening.

Beancounter offers the potential for a range of detailed analytics, including what you’ve watched and listened to most often and when, the things you are most interested in now and at previous points in time, people who have similar watching and listening habits, and those who are least similar. For more design inspiration we looked at many examples of beautiful data visualisations. We particularly liked the infographics from Hunch.com and The Feltron Annual Reports. However, many of these were hand-crafted, and our requirement is for attractive design modules that can be adapted for automated on-the-fly presentation.

Challenge 4: Interacting with multiple layers of information

The way that data is stored in the new Beancounter allows for any activity (e.g. listening to a Tom Waits track on Last.fm), interest (e.g. Tom Waits), type of interest (e.g. all people) or type of activity (e.g. all the things you’ve listened to) in the UI to be linked to the relevant analytics relating to that object; providing timelines, comparative views, explanations and statistics. Whilst this enables the user to delve deeper and gain extra insight to their data should they wish to, we want to make sure that the UI doesn’t become cluttered and confused with all these additional overlays. We’re therefore working with the visual designers to determine the most elegant model for interacting with these multiple layers of information without being overwhelmed by them.

Next steps

We’re still finalising the design work. Over the next few months we will be integrating these design mock-ups into a new Beancounter UI so that you will be able to try it out, get personalised TV recommendations in NoTube’s N-Screen, and perhaps discover some interesting new things about yourself…