Abstract: An electronic device and method for automatic generation of a sorted list of items related to a seed item comprises a relatedness determinator to compare the seed item with a plurality of further items and to determine a relatedness value for each further item with respect to the seed item. The device also has a clustering engine to cluster the further items by determining a relative relatedness between (among) the further items. Each further item is assigned to one cluster. The device also has a list generator to generate a sorted result list by sorting the further items according to both, their relatedness value and their belonging (or membership) to a cluster in that once an item is added to the sorted list, the relatedness value depending ranking of the further items in that cluster is at least momentary lowered so as to promote adding items of further clusters.

Abstract: The present invention is related to a recommender system (100), a computer-implemented recommending method, a corresponding computer readable medium and a corresponding computer program. A recommender system (100) is configured to send an electronic suggestion signal (186) to a respective user (199) of a user database (140) in dependence of a first like-degree (164) of a specific item, which has been determined based on items that the respective user has already recommended to his contacts. The electronic suggestion signal (186) suggests the respective user (199) to recommend the specific item to one or more of his contacts (198). In this way, personal based recommendations (188) are stimulated.

Abstract: The invention relates to an apparatus (100), a video system, a method and a corresponding computer program and a computer readable medium for managing a personal channel (210) of a user (200). The apparatus (100) is configured to control a search engine (300) with a specified search string (132) that the apparatus (100) has generated by combining a predefined search string (122) and a metadata piece (214). Thereby, the apparatus (100) retrieves a non-linear content (314) item being similar to a specific linear content item being referenced by the metadata piece and causes a recommendation (152) to the user (200) to add the retrieved non-linear content item to his personal channel (210).

Abstract: The present invention relates to an operating method of operating a recommender system, a filtering apparatus (260) for a recommender system (200), a recommender system and a corresponding computer program. An idea of the invention is to automatically learn for a user A in a social network, which recommendations of contacts of user A, who are also members of the social network, are relevant with respect to a genre into which user A is interested in. A learning algorithm is used to interpret feedback from user A in response to receiving recommendations from his/her contacts. Thereby, for each combination of a contact and a genre, a relevance-taste index can be determined. The determined relevance-taste index is subjected to a filter. Only such recommendations are provided to user A, whose associated relevance-taste indices fulfill a filtering criterion. Thereby, the amount of irrelevant recommendations submitted to user A can be significantly reduced.

Abstract: The present invention relates to an apparatus, a method and a computer program product for controlling a recommender system, wherein a user profile normally used by a recommender to predict user ratings is employed to generate a targeted query for the remote database yielding a set of results that can be scored by the recommender and provided as suggestions to the user.

Abstract: The present invention relates to an apparatus (100), a video system, a method and a corresponding computer program and a computer readable medium for managing a personal channel (210) of a user (200). The apparatus (100) is configured to identify a personal channel data piece that specifies a content item that the user (200) of the personal channel might not like anymore. The apparatus (100) then suggests (142) to the user (200) to delete the corresponding identified personal channel data piece and thus assists the user (200) in keeping a clear personal channel (210).

Abstract: A recommender engine for recommending content items to a user comprises a profile generation unit having a pre-profile input unit, which is configured to receive pre-profile data comprising pre-profile text data suitable for identifying entities of interest to a given user, and having a pre-profile analysis unit, the entities of interest and to generate an initial user profile data set for the given user from the extracted identification data; a query generation unit, to generate at least two queries semantically different from each other to be directed to at least one content repository; a content retrieval unit, to issue the generated queries to the content repository and to receive in response to the queries, content-related response data comprising respective hit lists and an interleaver unit to generate from the different hit lists a single recommendation list.

Abstract: A method and a device for adjusting filter parameters comprises a display, a physical user interface, a memory and a processing unit. The memory comprises a sorted list of items in an ordered manner, the order determined by their rank, and each item has at least one feature-value pair that represents a value of a feature of the item. The processing unit generates a graphical representation of the items in the list in an ordered manner on the display. The processing unit responds to the user interface so as to allow a user to re-order (rearrange) and/or drop items in the graphical representation of the list of items; it modifies the rank of the items in the list, and it determines a like-degree for at least some of the feature-value pairs.

Abstract: The present invention relates to a method of operating a recommender system arranged for being coupled to a computer implemented social network (200). The present invention furthermore relates to a corresponding computer program and to a corresponding recommender system (100) arranged for being coupled to a computer implemented social network (200). In particular, the present invention relates to a recommender system (100) being configured to provide a recommendation (172) relating to a content item being highly discussed in the social network (200), without the recommendation being necessarily based on some kind of a correlation between characteristics of a recipient (10) of the recommendation on the one side and characteristics of content items to be recommended and/or characteristics of contacts/friends of the recipient (10) on the other side.

Abstract: For the negotiation of an operator of a media-on-demand platform with its content providers a method and system for efficiently compiling media content items for the media-on-demand platform is provided. The method makes use of a combination of a recommender system to select a suitable set of media content items to be offered for a next period of service and a number-of-rentals predictor for estimating how many videos individual users will rent the following period of service. Furthermore, the method and system can be executed for estimating profit or loss from rentals over the following period of service as well as estimate customer satisfaction.

Abstract: A method of operating an apparatus for displaying content items on a display, a corresponding computer program for operating an apparatus and an apparatus for displaying content items on a display that is arranged for being coupled to the apparatus are provided. In particular, an apparatus is configured to display a plurality of content items in a two-dimensional grid in dependence of a respective relatedness between a selected content item and one of the content items to be displayed. The selected content item is positioned at a center of the display and the related content items are displayed such that associated relatedness values decrease both in left, right, up and down direction with respect to the center position. A user terminal that comprises such apparatus is provided.

Abstract: The present invention relates to an apparatus, a method and a computer program product for controlling a recommender system, wherein a user profile normally used by a recommender to predict user ratings is employed to generate a targeted query for the remote database yielding a set of results that can be scored by the recommender and provided as suggestions to the user.

Abstract: The present invention relates to an apparatus, a method and a computer program product for controlling a recommender system, wherein a user profile normally used by a recommender to predict user ratings is employed to generate a targeted query for the remote database yielding a set of results that can be scored by the recommender and provided as suggestions to the user.

Abstract: For the negotiation of an operator of a media-on-demand platform with its content providers a method and system for efficiently compiling media content items for the media-on-demand platform is provided. The method makes use of a combination of a recommender system to select a suitable set of media content items to be offered for a next period of service and a number-of-rentals predictor for estimating how many videos individual users will rent the following period of service. Furthermore, the method and system can be executed for estimating profit or loss from rentals over the following period of service as well as estimate customer satisfaction.

Abstract: A method and a device for adjusting filter parameters comprises a display, a physical user interface, a memory and a processing unit. The memory comprises a sorted list of items in an ordered manner, the order determined by their rank, and each item has at least one feature-value pair that represents a value of a feature of the item. The processing unit generates a graphical representation of the items in the list in an ordered manner on the display. The processing unit responds to the user interface so as to allow a user to re-order (rearrange) and/or drop items in the graphical representation of the list of items; it modifies the rank of the items in the list, and it determines a like-degree for at least some of the feature-value pairs.

Abstract: A device and a method for automatic generation of a recommendation for an item p to a user u. The device is at least indirectly connected to an item database containing a user profile database and a user interrelation database. A standard recommender engine accesses the item database and user profile database to calculate a score s(p,u) in a [0,1] interval indicating a “like” degree of an item p for user u based on a user profile of user u. The device further has a content discovery recommender to access the user interrelation database and to find at least users v that have a direct connection to user u to determine a fraction of the users v that know an item p and to generate a recommendation based on the score s(p,u) of item p.

Abstract: A recommender engine for recommending content items to a user comprises a profile generation unit having a pre-profile input unit, which is configured to receive pre-profile data comprising pre-profile text data suitable for identifying entities of interest to a given user, and having a pre-profile analysis unit, the entities of interest and to generate an initial user profile data set for the given user from the extracted identification data; a query generation unit, to generate at least two queries semantically different from each other to be directed to at least one content repository; a content retrieval unit, to issue the generated queries to the content repository and to receive in response to the queries, content-related response data comprising respective hit lists and an interleaver unit to generate from the different hit lists a single recommendation list.

Abstract: An electronic device and method for automatic generation of a sorted list of items related to a seed item comprises a relatedness determinator to compare the seed item with a plurality of further items and to determine a relatedness value for each further item with respect to the seed item. The device also has a clustering engine to cluster the further items by determining a relative relatedness between (among) the further items. Each further item is assigned to one cluster. The device also has a list generator to generate a sorted result list by sorting the further items according to both, their relatedness value and their belonging (or membership) to a cluster in that once an item is added to the sorted list, the relatedness value depending ranking of the further items in that cluster is at least momentary lowered so as to promote adding items of further clusters.

Abstract: The present invention relates to a method of operating an apparatus for displaying content items on a display, a corresponding computer program for operating an apparatus and to an apparatus (100) for displaying content items on a display (410) that is arranged for being coupled to the apparatus (100). In particular, the present invention relates to an apparatus (100) that is configured to display a plurality of content items in a two-dimensional grid (300) in dependence of a respective relatedness between a selected content item (A) and one of the content items to be displayed. The selected content item is positioned at a center of the display (410) and the related content items are displayed such that associated relatedness values decrease both in left, right, up and down direction with respect to the center position. The invention furthermore relates to a user terminal (400) that comprises such apparatus.

Abstract: The present invention relates to an operating method of operating a recommender system, a filtering apparatus (260) for a recommender system (200), a recommender system and a corresponding computer program. An idea of the invention is to automatically learn for a user A in a social network, which recommendations of contacts of user A, who are also members of the social network, are relevant with respect to a genre into which user A is interested in. A learning algorithm is used to interpret feedback from user A in response to receiving recommendations from his/her contacts. Thereby, for each combination of a contact and a genre, a relevance-taste index can be determined. The determined relevance-taste index is subjected to a filter. Only such recommendations are provided to user A, whose associated relevance-taste indices fulfil a filtering criterion. Thereby, the amount of irrelevant recommendations submitted to user A can be significantly reduced.