Recommender Systems Most recommender systems use a combination of different 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
Content-based Content-based recommenders use features such as the genre, cast 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.
Social Recommendations Social-networking technologies allow for a new level of 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.
Collaborative filtering Collaborative filter methods are based on collecting and 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).
Collaborative filtering Collaborative filtering systems can be categorized along the 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.
Collaborative filtering Similarity or distance function Memory-based systems and some 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.
Collaborative filtering User tasks for which collaborative filtering is useful 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.
Collaborative filtering Google’s PageRank mechanism is possible in the web because pages are linked to each other, but for TV we need to find another approach to relevance that will allow us to prioritise the most appropriate programming ahead of less relevant items.
The best algorithms take into account each of these factors: 1. Programme Information 2. Scheduling 3. Channel 4. Popularity 5. Viewer behaviour 6. Number of repeats and episodes
What else makes a good recommendation system? • Transparency: Explain 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.
Challenges The difficulty in implementing recommendations is that different users have different tastes and opinions about which television programmes they prefer.
Challenges Quality A substantial portion of the shows that are 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.
Challenges User feedback People are fanatical about television programmes and 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.
TiVo Recommendation Engine • Every show in the TiVo universe 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.
TiVo Recommendation Engine The flow of data starts with a 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.
TiVo Recommendation Engine The following sequence details the events leading 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
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