TV program recommendation for groups based on muldimensional TV-anytime classifications

Sotelo, Rafael; Blanco‐Fernández, Yolanda; López‐Nores, Martín; Gil‐Solla, Alberto; Pazos‐Arias, José J.

doi:10.1109/tce.2009.4814442

Cited by 46 publications

(30 citation statements)

References 14 publications

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“…However, if preference information is scarce, the accuracy of recommendation is unacceptably low. Typical collaborative recommendation systems are described in [2], [6]-[8]. Lee et al introduced a recommendation method with a preference program that uses collaborative filtering for each multi-agent, which is a client-server architecture [6].…”

Section: ⅱ Backgroundmentioning

confidence: 99%

“…Up-down buttons are used to sequentially search the channels, while agent-based channel recommendation systems are used to select the desired contents using a consumer' s profile and stored information regarding channel and program preferences [2]. Based on this customized information, personalized Electronic Program Guides (pEPGs) are generated and provided to consumers.…”

Section: ⅰ Introductionmentioning

confidence: 99%

“…Conventional approaches regarding channel recommendations are classified as either content-based channel recommendation approaches [3]- [5], cooperative channel recommendation approaches [2], [6]- [8], hybrid filtering approaches that provide content-based filters by cooperative channel filtering [9], [10], client-server based channel recommendation approaches for next generation EPG systems [11], [12], and IP-based personalized mosaic EPG service [19]. Some studies have also been conducted to reduce the zapping time required to search for popular channels [1], [16]- [18].…”

Section: ⅰ Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Popular Channel Surfing Scheme for Reducing the Channel Seek Distance in DTV

Lee¹,

Choi²

2011

Journal of the Korea Society of Computer and Information

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Due to the increasing availability and popularity of digital television (DTV), the numbers of TV channels and programs that can be selected by consumers are also increasing rapidly. Therefore, searching for interesting channels and program via remote controls or channel guide maps can be frustrating and slow. In this paper, in order to better satisfy consumers, we propose a dynamic channel surfing scheme that reduces the channel seek distance in DTV. The proposed scheme dynamically rearranges the channel sequences according to the channel currently being watched to reduce the channel seek distance. The results of a simulation experiment demonstrate that the proposed dynamic channel surfing scheme reduces the channel seek distance for DTV channel navigation when up-down channel selection interfaces are used.

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Section: ⅱ Backgroundmentioning

confidence: 99%

Section: ⅰ Introductionmentioning

confidence: 99%

Section: ⅰ Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Popular Channel Surfing Scheme for Reducing the Channel Seek Distance in DTV

Lee¹,

Choi²

2011

Journal of the Korea Society of Computer and Information

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“…Researchers evaluate and verify group recommendation approaches, however, with synthetic or limited datasets [8][9][10][11][12][13][14][15][16] because real large-scale datasets of group profiles are rare. Regarding design group recommendation systems, we need to consider group and domain characteristics simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Group Recommendation Systems in TV Domains

Kim

Lee

2015

Int. J. Fuzzy Log. Intell. Syst.

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Although researchers have proposed various recommendation systems, most recommendation approaches are for single users and there are only a small number of recommendation approaches for groups. However, TV programs or movies are most often viewed by groups rather than by single users. Most recommendation approaches for groups assume that single users' profiles are known and that group profiles consist of the single users' profiles. However, because it is difficult to obtain group profiles, researchers have only used synthetic or limited datasets. In this paper, we report on various group recommendation approaches to a real largescale dataset in a TV domain, and evaluate the various group recommendation approaches. In addition, we provide some guidelines for group recommendation systems, focusing on home group users in a TV domain.

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“…It aims at recommending items that users had not yet painstaking, but are likely to be favored. Recommender systems can be generally divided into three types: collaborative filtering [5], content-based filtering [6] and hybrid approach, which uses both of the two methods [7][8] [9]. Collaborative filtering recommends contents by analyzing the common patterns of multiple users who contribute to the same interests [10][11] [12].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Personalized Multimedia Content Search

Vanashri¹

2015

IJRITCC

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Abstract-Millions of users share opinions on different aspects of life every day. User can give opinions through their comments related to multimedia contents. For analyzing user's demands we can evaluate user comments as a common online resource. Comments given b y user provide clues about them & also their interest area. In this paper, we done survey on different techniques used for personalized search in relevant information retrieval. Recommender systems or recommendation systems are a subclass of information filtering system that seek to predict 'rating' or 'preference' that are given by users. We propose a personalized search system, called MovieMine, enhance this proposed system to provide more relevant data summarized according to past & present comments left by user. Large movie data center provide movie review which can be utilized for more specific output. Experimental evaluations show that our proposed techniques are efficient and perform better than previously proposed methods. As in research work we can study more types of sentiment analysis with different recommender systems approach.

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TV program recommendation for groups based on muldimensional TV-anytime classifications

Cited by 46 publications

References 14 publications

Dynamic Popular Channel Surfing Scheme for Reducing the Channel Seek Distance in DTV

Dynamic Popular Channel Surfing Scheme for Reducing the Channel Seek Distance in DTV

Performance Analysis of Group Recommendation Systems in TV Domains

A Survey on Personalized Multimedia Content Search

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