Development and features of a TV navigation system

Isobe, Tadashi; Fujiwara, Motoyoshi; Kaneta, Hiroyuki; Uratani, Noriyoshi; Morita, Toshiya

doi:10.1109/icce.2003.1218817

Cited by 8 publications

(6 citation statements)

References 1 publication

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“…TV program recommendation systems have been studied for realizing personalized TV guides [2,4,8,9,10]. In order to recommend appropriate TV programs, a user profile that reflects his / her preference on selecting TV programs should be estimated.…”

Section: Related Workmentioning

confidence: 99%

“…Users' behaviors of watching TV programs (viewing behaviors) have also been investigated [1,4,7]. In the studies, various types of viewing behaviors, such as concerned viewing and diversion viewing, are observed according to period of time in a day [4,7].…”

Section: Related Workmentioning

confidence: 99%

“…In the studies, various types of viewing behaviors, such as concerned viewing and diversion viewing, are observed according to period of time in a day [4,7]. It is also observed that user's watching behavior in weekday is more stable than that in weekend [1,7].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Profile Generation from TV Watching Behavior Using Sentiment Analysis

Takama¹,

Muto²

2007

2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops

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This paper proposes a method for generating user profile from user's TV watching behavior using sentiment analysis. Personalized technologies such as information recommendation are currently hot topic of Web intelligence. Among them, TV program recommendation is expected to be one of the practical applications in near future, as digital TV service and partner robots providing personalized support are getting into our living environment. The proposed method does not estimate user's interest in a TV program only from its watching time as most of existing methods do, but also from user's utterances by applying sentiment analysis. The method employs fuzzy inference for estimating the user's rating of a TV program. A user profiles with bookmark format is generated based on the estimated rating of TV programs. Experiments are performed by collecting TV watching logs with diary-based approach, and the results show the proposed method can generate a user profile that can reflect user's interests.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Profile Generation from TV Watching Behavior Using Sentiment Analysis

Takama¹,

Muto²

2007

2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops

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“…According to the increasing amount of services provided by the DTV, conventional methods of channel selection such as browsing become impractical [1]. The electronic program guide (EPG) can help viewers check future programs in advance.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Person Identification System for Intelligent Digital TV

Hwang

Kim

et al. 2007

2007 Digest of Technical Papers International Conference on Consumer Electronics

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Intelligent digital TV (iDTV) is an enhanced digital TV that can automatically provide user-personalized services for each audience. For the user-personalized services, the iDTV should recognize audiences in real-time. Thus, in this paper, we define a novel structure of the iDTV and propose a real-time person identification system embedded in the iDTV. The proposed system consists of three phases: preprocessing for reducing computational costs of the proposed system, face detection using a statistical approach with Haar-like features, and face recognition using Support Vector Machines (SVMs). Experimental results show that the proposed system achieves efficient performance with high recognition accuracy of above 90% at the speed of 15~20 fps, which is suitable for applying to the iDTV.

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“…TV navigation systems make it easier for viewers to find and watch TV programs, as described in [8].…”

Section: B Accessible Program Guidementioning

confidence: 99%