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 method for providing individualized recommendations to a user on a multi-user device is provided. During operation anonymous user preferences of similar program content will be grouped to form clusters of similar preferences. Context information for each cluster is determined and the clusters are grouped to form larger clusters. The grouping is based on the context information for each cluster. A current context is then determined and at least one larger cluster is found that has a similar context as the current context. The larger cluster is used to make a recommendation for the user.

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.

Abstract: An apparatus for content item recommendation, such as a Digital Video Recorder, comprises a grouping unit (105) for grouping user ratings for content items into rating groups in response to a content item match criterion. A receiver (109) receives content item data for a plurality of content items and a first recommendation unit (107) generating a first set of content item recommendations. An association unit (111) then determines an associated rating group of the rating groups for each content item recommendation of the first set and a second recommendation unit (113) generates a second set of content item recommendations from the first set in response to a rating group distribution measure for the second set. The invention may allow improved recommendation of content items which is aligned with user preferences yet provide a desired diversity of the provided recommendation. The invention may in particular provide improved performance for multi-user devices.

Abstract: A method of generating a user profile initially comprises receiving (201, 203) characterising data, and optionally user preferences, for content items. The characterising data describes characteristics, such as content or context characteristics, of each content item. The content items are then clustered (205) into content item clusters in response to characterising data associated with each content item. For each content item cluster, cluster characterising data is determined (207) in response to characterising data and possibly user preferences associated with each content item in the content item cluster. First characterising data is then received (209) for a first content item and a first content item cluster is selected (211) in response to a comparison of the first characterising data and the cluster characterising data of each content item cluster. A user profile is then generated (211) for the first content item in response to first cluster characterising data of the first content item cluster.

Abstract: A distributed content item recommendation system comprises a central recommendation server (101) and a plurality of remote recommendation devices (103) coupled to the central recommendation server (101) via a communication network (105). The central recommendation server (101) stores content item set correlation data for sets of content items. The correlation data is used for item based collaborative filtering in recommendation processors (303) of the recommendation devices (103). A computation task processor (207) maintains a task list of content item correlation computation tasks which can be independently executed to generate content item set correlation data. A task assignment processor (209) can assign the computation tasks to remote recommendation devices (103) which comprise a processing unit (307) that calculates the associated correlation data and returns it to the recommendation server (101).