The key idea behind TV recommendations is filtering interesting programmes and bringing them to the user’s attention, which is becoming increasingly relevant to all TV watchers as channels proliferate.
Why was this of interest to NoTube?
NoTube set out to produce personalised content recommendations, using data models of users and contexts.
People often have several clusters of personal data on the Web, such as their profiles on social networks and ratings on YouTube and IMDB. Analogously to the personal data, there are many isolated clusters of broadcast data on the Web, such as broadcast data on electronic programme guides (EPGs) and background knowledge on Wikipedia.
NoTube asserts that the conjunction of all these bits and pieces of data can provide in- valuable information about someone’s interests and the TV broadcasts that should be recommended to them. Re-using existing online user data potentially solves the notorious ‘cold start’ problem, i.e. that a recommendation system cannot provide useful recommendations until a substantial amount of statistical user activity data has been gathered.
The NoTube vision was to realise a recommendation system that uses all these bits and pieces of data in conjunction. Semantic Web standards are important building blocks, since they enable the global identification mechanism of URIs and the means to define relations between data anywhere on the Web.
What NoTube has done in this area
Personalised recommendations based on a weighted interests User Profile
For these types of recommendations the user must have created a machine-processible User Profile from their existing social activity data using the NoTube Beancounter services. Beancounter services generate a User Profile of weighted interests from enriched user activity data. Try it!
You can read about Beancounter focus group testing and user reactions to the Beancounter interface, and the code is available in github.
Programme-to-programme recommendations based on interesting links
We were interested in serendipitous browsing of large video collections, so we are looking at ways to help users navigate through such collections by following interesting connections between programmes.
For example, for our BBC Archive Browser demo, the connections were created using a BBC-specific subject classification system, traditionally used by professional cataloguers for subject indexing/classification as part of the cataloguing procedure in the BBC’s internal TV and radio programme catalogue. The results of our user evaluation of this demo suggested to us that re-using existing programme metadata to navigate large video collections (including archives, on-demand content and EPGs) could help with the ‘cold start’ problem.
Providing explanations
Presenting the pathways through graphs allows people to see the connection that led to a recommendation being made. An explanation based on an interesting story can potentially make a recommendation more satisfying to the user.
Find out more
Watch this video about NoTube’s hybrid approach to recommendations:
A short (2 minute) version of this video is available in various languages: English, French, Italian, and Portuguese.
Who was involved? Vrije Universiteit in Amsterdam, Pronetics, BBC Research & Development, in collaboration with other project partners.
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