Abstract: A recommendation apparatus comprises a monitoring processor which monitors the presentation of content items. A sample processor determines preference data for different content items by performing the steps of determining a preference value for a content item presented by the presentation unit in response to a first duration for a first section of the content item being presented relative to a total duration of the content item, and if the first duration is less than the total duration, determining if a second section of the content item not being presented corresponds to at least one of an end section and a begin section of the content item; and if so determining a confidence value for the preference value in response to a second duration of the second section. The preference data is used as training data for determining a user preference model which is then used to generate recommendations.

Abstract: A recommendation apparatus comprises a monitoring processor which monitors the presentation of content items. A sample processor determines preference data for different content items by performing the steps of determining a preference value for a content item presented by the presentation unit in response to a first duration for a first section of the content item being presented relative to a total duration of the content item, and if the first duration is less than the total duration, determining if a second section of the content item not being presented corresponds to at least one of an end section and a begin section of the content item; and if so determining a confidence value for the preference value in response to a second duration of the second section. The preference data is used as training data for determining a user preference model which is then used to generate recommendations.

Abstract: A recommendation system comprises a plurality of recommenders for generating recommendations in accordance with a user preference profile having a recommender specific representation. The recommender specific representation is different for different recommenders and each recommender comprises translation data relating the recommender specific representation to a shared ontology. A recommender comprises a translation unit which generates first user preference data in accordance with the shared ontology in response to a user preference profile and translation data relating the recommender specific representation to the shared ontology. In addition, a confidence indication for at least part of the user preference data is generated. A transmitter transmits the user preference data and the confidence indication to a second recommender. The second recommender may translate the received data into its recommender specific representation and combine the received data with locally generated user preference data.

Abstract: An apparatus for generating content program recommendations comprises a meta-data processor (209) which provides characterising data for a plurality of content programs. A context processor (215) provides first context data for a user and a time window processor (213) determines a content consumption time window for the user in response to the first context data. The content consumption time window represents an estimated time window available to the user for consuming content. A recommendation unit (207) then generates a content program recommendation comprising in response to the content consumption time window and the characterising data. The invention may be particularly advantageous for mobile content program distribution services, such as a mobile television service, as recommendations may be generated that take into account the particular characteristics of the mobile user environment.