MEDIA CONTENT RECOMMENDATION AND USER INTERFACE GENERATION
A method, a device, and a non-transitory storage medium are described in which a personalized content recommendation system determines a content-offering value (COV) for each title of content identified in a content catalog, wherein the COVs indicate terms of offerings to a user for consuming each t...
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| creator | Bandaru, Praveen Kumar Raghavan, Senthil K Srinivasaraghavan, Haripriya |
| description | A method, a device, and a non-transitory storage medium are described in which a personalized content recommendation system determines a content-offering value (COV) for each title of content identified in a content catalog, wherein the COVs indicate terms of offerings to a user for consuming each title of content; calculate, for each title of content, a content-relevance value (CRV), wherein the CRV indicates respective relevancies of each title of content to the user; calculate, for each title of content, a cost-content sensitivity index (CCSI) value indicative of the user's relative cost and content sensitivities, wherein the CCSI value is calculated for a time-of-day parameter or a content-genre parameter for consuming each title of content; calculate, for each title of content, a cost-content tradeoff score (CCTS) based on the COV, CRV, and CCSI value; and identify k number of titles of content having the highest CCTS. |
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calculate, for each title of content, a content-relevance value (CRV), wherein the CRV indicates respective relevancies of each title of content to the user; calculate, for each title of content, a cost-content sensitivity index (CCSI) value indicative of the user's relative cost and content sensitivities, wherein the CCSI value is calculated for a time-of-day parameter or a content-genre parameter for consuming each title of content; calculate, for each title of content, a cost-content tradeoff score (CCTS) based on the COV, CRV, and CCSI value; and identify k number of titles of content having the highest CCTS.</description><language>eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200206&DB=EPODOC&CC=US&NR=2020042605A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200206&DB=EPODOC&CC=US&NR=2020042605A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Bandaru, Praveen Kumar</creatorcontrib><creatorcontrib>Raghavan, Senthil K</creatorcontrib><creatorcontrib>Srinivasaraghavan, Haripriya</creatorcontrib><title>MEDIA CONTENT RECOMMENDATION AND USER INTERFACE GENERATION</title><description>A method, a device, and a non-transitory storage medium are described in which a personalized content recommendation system determines a content-offering value (COV) for each title of content identified in a content catalog, wherein the COVs indicate terms of offerings to a user for consuming each title of content; 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calculate, for each title of content, a content-relevance value (CRV), wherein the CRV indicates respective relevancies of each title of content to the user; calculate, for each title of content, a cost-content sensitivity index (CCSI) value indicative of the user's relative cost and content sensitivities, wherein the CCSI value is calculated for a time-of-day parameter or a content-genre parameter for consuming each title of content; calculate, for each title of content, a cost-content tradeoff score (CCTS) based on the COV, CRV, and CCSI value; and identify k number of titles of content having the highest CCTS.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | MEDIA CONTENT RECOMMENDATION AND USER INTERFACE GENERATION |
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