Personal TVware: An Infrastructure to Support the Context-Aware Recommendation for Personalized Digital TV

Silva, Fábio Santos da; Alves, Luiz Gustavo Pacola; Bressan, Graça

doi:10.7763/ijcte.2012.v4.437

Cited by 10 publications

(9 citation statements)

References 9 publications

(8 reference statements)

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“…Most of these systems disregarded users' time context and time-dependent watching patterns. As users' time context is an important factor for TV watching, other TV recommender systems use time context as input, which is a time-aware system [13][14][15][16][17][18][19][20][21]. In this section, these two types of TV recommender systems are surveyed.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, TV recommender systems considering time-aware consumption patterns have been actively researched [13][14][15][16][17][18][19][20][21]. For example, Oh et al proposed a time-dependent user profiling technique that splits a user's watching logs into certain time slots and re-merges consecutive time slots by using a hierarchical clustering method [13].…”

Section: Time-aware Tv Recommender Systemsmentioning

confidence: 99%

“…It is a kind of information filtering system that predicts users' ratings or preferences for items and provides users with items that they may be interested in. It is applied in a variety of domains such as movies, music, books, news, and any other contents in general [1,4,18,20,22,[25][26][27][28][29]. As a result, many recommendation algorithms have been developed for these domains in recent years.…”

Section: Introductionmentioning

confidence: 99%

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A Television Recommender System Learning a User’s Time-Aware Watching Patterns Using Quadratic Programming

Kim

Lee

2018

Applied Sciences

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In this paper, a novel television (TV) program recommendation method is proposed by merging multiple preferences. We use channels and genres of programs, which is available information in standalone TVs, as features for the recommendation. The proposed method performs multi-time contextual profiling and constructs multiple-time contextual preference matrices of channels and genres. Since multiple preference models are constructed with different time contexts, there can be conflicts among them. In order to effectively merge the preferences with the minimum number of conflicts, we develop a quadratic programming model. The optimization problem is formulated with a minimum number of constraints so that the optimization process is scalable and fast even in a standalone TV with low computational power. Experiments with a real-world dataset prove that the proposed method is more efficient and accurate than other TV recommendation methods. Our method improves recommendation performance by 5-50% compared to the baselines.

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Section: Related Workmentioning

confidence: 99%

Section: Time-aware Tv Recommender Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Television Recommender System Learning a User’s Time-Aware Watching Patterns Using Quadratic Programming

Kim

Lee

2018

Applied Sciences

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“…Different solutions based on explicit or implicit ratings or collaborative, content-based or hybrid methods have been proposed for the TV recommendation problem [1,11,12]. The TV movie recommendation task to a user on a certain day has the following challenges, which may not be the case for most of the other recommendation tasks.…”

Section: Introductionmentioning

confidence: 99%

Feature selection for movie recommendation

Çataltepe¹,

Uluyagmur²,

Tayfur³

2016

Turk J Elec Eng & Comp Sci

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TV users have an abundance of different movies they could choose from, and with the quantity and quality of data available both on user behavior and content, better recommenders are possible. In this paper, we evaluate and combine different content-based and collaborative recommendation methods for a Turkish movie recommendation system. Our recommendation methods can make use of user behavior, different types of content features, and other users' behavior to predict movie ratings. We gather different types of data on movies, such as the description, actors, directors, year, and genre. We use natural language processing methods to convert the Turkish movie descriptions into keyword vectors. Then, for each user, we use the content features and the user's past implicit ratings to produce content feature-based user profiles. In order to have more reliable profiles, we do feature selection on these profiles. We show that for each feature space, such as actor, director, or keyword, a different amount of feature selection may be optimal. Different recommenders may also perform best for a different number of movies available as training data for a user. We also combine different content-based recommenders and collaborative recommenders using an aggregation or the best of the available recommenders. Experimental results on a dataset with hundreds of users and movies show that, especially for users who have watched a small number of movies in the past, feature selection can increase recommendation success.

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“…No decorrer desta pesquisa alguns trabalhos foram identificados como possíveis propostas de trabalhos futuros. O arcabouço geral da pesquisa desta tese e os resultados finais obtidos foram publicados em (SILVA et al, 2011). Em (SILVA et al, 2010a) foi descrito o projeto e a implementação do PersonalTVware.…”

Section: Trabalhos Futurosunclassified

PersonalTVware: uma infraestrutura de suporte a sistemas de recomendação sensíveis ao contexto para TV Digital Personalizada.

Silva¹

Self Cite

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user's device and the service provider. The task of inferring contextual preferences is based on machine learning methods, and context-aware information filtering is based on content-based filtering technique. The concept of contextual user profile is presented and discussed. To demonstrate the functionalities in a usage scenario a context-aware recommender system was developed as a case study applying the PersonalTVware.

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Personal TVware: An Infrastructure to Support the Context-Aware Recommendation for Personalized Digital TV

Cited by 10 publications

References 9 publications

A Television Recommender System Learning a User’s Time-Aware Watching Patterns Using Quadratic Programming

A Television Recommender System Learning a User’s Time-Aware Watching Patterns Using Quadratic Programming

Feature selection for movie recommendation

PersonalTVware: uma infraestrutura de suporte a sistemas de recomendação sensíveis ao contexto para TV Digital Personalizada.

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