Community-based program recommendation for the next generation electronic program guide

Recommending TV content to groups of viewers is best carried out when relevant information such as the demographics of the group is available. However, it can be difficult and time consuming to extract information for every user in the group. This paper shows how an audio analysis of the age and gender of a group of users watching the TV can be used for recommending a sequence of N short TV content items for the group. First, a state of the art audio-based classifier determines the age and gender of each user in an M-user group and creates a group profile. A genetic recommender algorithm then selects for each user in the profile, a single personalized multimedia item for viewing. When the number of items to be presented is different to the number of viewers in the group, i.e. M ≠ N, a novel adaptation algorithm is proposed that first converts the M-user group profile to an N-slot content profile, thus ensuring that items are proportionally allocated to users with respect to their demographic categorization. The proposed system is compared to an ideal system where the group demographics are provided explicitly. Results using real speaker utterances show that, in spite of the inaccuracies of state-of-the-art age-and-gender detection systems, the proposed system has a significant ability to predict an item with a matching age and gender category. User studies were conducted where subjects were asked to rate a sequence of advertisements, where half of the advertisements were randomly selected, and the other half were selected using the audio-derived demographics. The recommended advertisements received a significant higher median rating of 7.75, as opposed to 4.25 for the randomly selected advertisements 1 .

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“…, i. e. user partition size in bins (11) and where 2 The adaptation for each age and gender class is computed independently of the others.…”

Section: Group Profile Adaptationmentioning

confidence: 99%

“…through registration questionnaires [1] or by asking users to provide ratings. Data can also be collected implicitly through usage patterns [2], [3], and subsequently fed to a central information server, which can then make recommendations.…”

Section: Introductionmentioning

confidence: 99%

Audio-based age and gender identification to enhance the recommendation of TV content

Shepstone

Jensen

2013

IEEE Trans. Consumer Electron.

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“…It is calculated as the ratio of the amount of recommended content in which the user is interested to the amount of recommended content [11].…”

Section: ) Epg Browsing Time (Ebt)mentioning

confidence: 99%

Enriched IPTV services personalization

Song

Moustafa

Afifi

2012

2012 IEEE International Conference on Communications (ICC)

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The advances in IPTV (Internet Protocol Television) technology enable new user-centric and interactive TV model, in which context-awareness is promising in making users' interaction with the TV dynamic and transparent. This paper deals with the problem of achieving user-centric personalized IPTV services applying context-awareness. We present a solution for enriched IPTV services personalization introducing contextawareness on top of IPTV architecture on one hand to gather different information on the user and his environment and allowing each user to be distinguished to the system in a unique and real-time manner on the other hand. We implemented the proposed solution on top of an IPTV platform considering the NGN IPTV architecture as a proof of concept and as a means to evaluate the performance.

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“…EPG information enables further analyses of event content and event associations [1]. The processed information is then used as an input for notifications and alerts [2] or smart recommendation engines.…”

Section: Introductionmentioning

confidence: 99%

One implementation of extendable application for collecting EPG data from internet sources

Pejic

Tukuljac

Knezevic

et al. 2014

2014 IEEE Fourth International Conference on Consumer Electronics Berlin (ICCE-Berlin)

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Community-based program recommendation for the next generation electronic program guide

Cited by 22 publications

References 7 publications

Audio-based age and gender identification to enhance the recommendation of TV content

Audio-based age and gender identification to enhance the recommendation of TV content

Enriched IPTV services personalization

One implementation of extendable application for collecting EPG data from internet sources

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