approaches, but broadly speaking there are three different methods that can be used: Content analysis and extraction of common patterns Social recommendations based on personal choices from other people Collaborative filtering of different users behaviour, preferences, and ratings
and age of the show as attributes for a learning system. However, such features are only weakly predictive of whether viewers will like the show. In the TV world, the only content-analysis technologies available to date rely on the metadata associated with the programmes. The recommendations are only as good as the metadata.
sophistication whereby users can easily receive recommendations based on the shows that other people within their social network have ranked highly. Social recommendations provide a more personal level of recommendations. The advantage of social recommendations is that because they have a high degree of personal relevance they are typically well received, with the disadvantage being that the suggested shows tend to cluster around a few well known or cult-interest programmes.
analysing a large amount of information on users’ behaviour, activity or preferences and predicting what users will like based on their similarity to other users. Passive filtering Provides recommendations based on activity without explicitly asking the users’ permission (e.g. Amazon). Active filtering Uses the information provided by the user as the basis for recommendations (e.g. Netflix).
following major dimensions: User-user or item-item systems In user-user systems, correlations (or similarities or distances) are computed between users. In item-item systems metrics are computed between items (e.g. shows or movies). Form of the learned model Most collaborative filtering systems to date have used k-nearest neighbour models in user-user space. However there has been work using other model forms such as Bayesian networks, decision trees, cluster models and factor analysis.
others need to define a distance metric between pairs of items or users. The most popular and one of the most effective measures used to date has been the simple and obvious Pearson product moment correlation coefficient (PMCC). Combination function Having defined a similarity metric between pairs of users or items, the system needs to make recommendations for the active user for an unrated item. Memory-based systems typically use the k-nearest neighbour formula.
1. Help me find new items I might like. 2. Advise me on a particular item. 3. Help me find a user I might like. 4. Help our group find something new that we might like. 5. Help me find a mixture of "new" and "old" items. 6. Help me with tasks that are specific to this domain.
how the system works. • Scrutability: Allow users to tell the system it is wrong. • Trust: Increase users confidence in the system. • Persuasiveness: Convince users to try or buy. • Effectiveness: Help users make good decisions. • Satisfaction: Make the use of the system fun.
recommended to the user should be shows that they would like to watch, or at least might find interesting. Transparency It should be clear to the user why they have been recommended certain shows so that if they have been recommended a show they don’t like they can at least understand why.
if they are being recommended a show that they don’t like they should have an immediate way to say that they don’t like it and subsequently never have it recommended again. Driving take-up The recommendations needs to drive the take up of the shows that they are recommending. This can only be measured by monitoring the shows that are recommended and seeing how user behaviours change.
has a unique identifying series ID assigned by Tribune Media Services (TMS). • Shows come in two types: movies and series which are recurring programs such as 'Friends'. • A series consists of a set of episodes. All episodes of a series have the same series ID. • Prediction is made at the series level so TiVo does not currently try to predict whether you will like one episode more than another.
user rating a show. There are two types of rating: 1. Explicit feedback: The viewer can use the thumbs-up and thumbs-down buttons on the TiVo remote control to indicate if she likes the show. 2. Implicit feedback: Since various previous collaborative filtering systems have noted that users are very unlikely to volunteer explicit feedback, in order to get sufficient data the only user action that results in an implicit rating happens when the user choose to record a previously unrated show.
to TiVo making a show suggestion for the viewer: 1. Viewer feedback 2. Transmit profile 3. Anonymization 4. Server-side computation 5. Correlation download 6. Client-side computation 7. Suggestions list 8. Inferred recordings