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1. WO2020205647 - SYSTEMS AND METHODS FOR PROVIDING MEDIA CONTENT RECOMMENDATIONS

Publication Number WO/2020/205647
Publication Date 08.10.2020
International Application No. PCT/US2020/025500
International Filing Date 27.03.2020
IPC
G06F 16/74 2019.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
70of video data
74Browsing; Visualisation therefor
G06F 16/735 2019.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
70of video data
73Querying
735Filtering based on additional data, e.g. user or group profiles
H04N 21/25 2011.01
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
NPICTORIAL COMMUNICATION, e.g. TELEVISION
21Selective content distribution, e.g. interactive television or video on demand
20Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
25Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
CPC
G06F 16/735
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
70of video data
73Querying
735Filtering based on additional data, e.g. user or group profiles
G06F 16/74
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
70of video data
74Browsing; Visualisation therefor
G06N 5/02
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computer systems using knowledge-based models
02Knowledge representation
H04N 21/251
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
NPICTORIAL COMMUNICATION, e.g. TELEVISION
21Selective content distribution, e.g. interactive television or video on demand [VOD]
20Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
25Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
251Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/4662
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
NPICTORIAL COMMUNICATION, e.g. TELEVISION
21Selective content distribution, e.g. interactive television or video on demand [VOD]
40Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
45Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
466Learning process for intelligent management, e.g. learning user preferences for recommending movies
4662characterized by learning algorithms
H04N 21/4668
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
NPICTORIAL COMMUNICATION, e.g. TELEVISION
21Selective content distribution, e.g. interactive television or video on demand [VOD]
40Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
45Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
466Learning process for intelligent management, e.g. learning user preferences for recommending movies
4668for recommending content, e.g. movies
Applicants
  • ROVI GUIDES, INC. [US]/[US]
Inventors
  • MILLER, Kyle
  • SCAPPINI, Bryan, S.
  • LENT, James, W.
Agents
  • TRAINOR, Thomas P.
  • DEREVJANIK, Mario
  • GUILIANO, Joseph, M.
  • FEUSTEL, Richard, M.
  • ARPIN, Anthony, J.
Priority Data
16/370,10129.03.2019US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) SYSTEMS AND METHODS FOR PROVIDING MEDIA CONTENT RECOMMENDATIONS
(FR) SYSTÈMES ET PROCÉDÉS PERMETTANT DE FOURNIR DES RECOMMANDATIONS DE CONTENU MULTIMÉDIA
Abstract
(EN)
Systems and associated methods are described for providing content recommendations. The system accesses a plurality of recommendation algorithms and assigns a plurality of weight values to each prediction algorithm. Then, the system generates a set of candidate weight combinations, such that each candidate combination includes a weight value assigned to each prediction algorithm. Then requests for content items are received over a predetermined period of time. For each combination, the system generates a set of recommended content items and an evaluation metric that is based on matches with requests. Afterwards, the system replaces a candidate combination that resulted in a generation of a lowest evaluation metric. The aforementioned steps are repeated until the evaluation metrics stop improving. Then display identifiers are displayed for a set of recommended content items generated for a candidate combination with the highest evaluation metric.
(FR)
L'invention concerne des systèmes et des procédés associés permettant de fournir des recommandations de contenu. Le système a accès à une pluralité d'algorithmes de recommandation et attribue une pluralité de valeurs de pondération à chaque algorithme de prédiction. Ensuite, le système génère un ensemble de combinaisons de poids de candidat de telle sorte que chaque combinaison de candidats comprenne une valeur de pondération attribuée à chaque algorithme de prédiction. Ensuite, des demandes d'éléments de contenu sont reçues sur une période de temps prédéterminée. Pour chaque combinaison, le système génère un ensemble d'éléments de contenu recommandés et une métrique d'évaluation qui est basée sur des correspondances avec des demandes. Par la suite, le système remplace une combinaison de candidats qui a conduit à une génération de la métrique d'évaluation la plus faible. Les étapes susmentionnées sont répétées jusqu'à ce que les métriques d'évaluation s'améliorent. Ensuite, des identifiants d'affichage sont affichés pour un ensemble d'éléments de contenu recommandés générés pour une combinaison de candidats ayant la métrique d'évaluation la plus élevée.
Latest bibliographic data on file with the International Bureau