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Pogacnik et al., 2005 - Google Patents

Personal content recommender based on a hierarchical user model for the selection of TV programmes

Pogacnik et al., 2005

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Document ID
9641397144769765960
Author
Pogacnik M
Tasic J
Meza M
Kosir A
Publication year
Publication venue
User Modeling and User-Adapted Interaction

External Links

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In this paper we present our approach to user modeling for a personalized selection of multimedia content tested on a corpus of TV programmes. The idea of this approach is to classify content (TV programmes) based on the calculation of similarities between the …
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Classifications

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    • G06FELECTRICAL DIGITAL DATA PROCESSING
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    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30017Multimedia data retrieval; Retrieval of more than one type of audiovisual media
    • G06F17/30023Querying
    • G06F17/30029Querying by filtering; by personalisation, e.g. querying making use of user profiles
    • G06F17/30035Administration of user profiles, e.g. generation, initialisation, adaptation, distribution
    • GPHYSICS
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    • G06F17/30017Multimedia data retrieval; Retrieval of more than one type of audiovisual media
    • G06F17/30023Querying
    • G06F17/30038Querying based on information manually generated or based on information not derived from the media content, e.g. tags, keywords, comments, usage information, user ratings
    • GPHYSICS
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    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861Retrieval from the Internet, e.g. browsers
    • G06F17/30864Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
    • G06F17/30867Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
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    • H04N21/47End-user applications
    • H04N21/482End-user interface for program selection
    • H04N21/4826End-user interface for program selection using recommendation lists, e.g. of programs or channels sorted out according to their score
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
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    • H04N21/466Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4668Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies
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    • G06F17/30781Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F17/30817Information retrieval; Database structures therefor; File system structures therefor of video data using information manually generated or using information not derived from the video content, e.g. time and location information, usage information, user ratings
    • GPHYSICS
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    • H04N21/466Learning process for intelligent management, e.g. learning user preferences for recommending movies
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    • G06F17/30784Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
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    • H04N21/442Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
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    • H04N21/44222Monitoring of user selections, e.g. selection of programs, purchase activity
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    • H04N21/80Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
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    • H04N21/251Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/252Processing of multiple end-users' preferences to derive collaborative data

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