TV Recommendation and Personalization Systems: Integrating Broadcast and Video On demand Services

Soares, Márcio Micael; Viana, Paula

doi:10.4316/aece.2014.01018

Cited by 16 publications

(10 citation statements)

References 7 publications

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“…Then it provides the recommendations to the groups based on relevance degrees of the graph. TV Recommendation and personalization Systems [18] is a web-based group recommendation system which recommends the TV program for more than one user by merging user profiles with the help of total distance minimization technique. Clustering methods can be clubbed within the group recommendation systems to detect and form a group with the help of various group modeling strategies.…”

Section: Related Workmentioning

confidence: 99%

A Novel Rule based Data Mining Approach towards Movie Recommender System

Sharma

Ahuja

Kumar

2020

JIOS

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The proposed research work is an effort to provide accurate movie recommendations to a group of users with the help of a rule-based content-based group recommender system. The whole approach is categorized into 2 phases. In phase 1, a rule- based approach has been proposed which considers the users’ viewing history to provide the Rule Base for every individual user. In phase 2, a novel group recommendation system has been proposed which considers the ratings of the movies as per the rule base generated in phase 1. Phase 2 also considers the weightage of every individual member of the group to provide the accurate movie recommendation to that particular group of users. The results of experimental setup also establish the fact that the proposed system provides more accurate outcomes in terms of precision and recall over other rule learning algorithms such as C4.5.

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Section: Related Workmentioning

confidence: 99%

A Novel Rule based Data Mining Approach towards Movie Recommender System

Sharma

Ahuja

Kumar

2020

JIOS

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“…Beyond these examples, recommender systems also apply on numerous other domains including books 11 , movies and TV programs 45,46 , music 22,31 , news articles 6,49 , and so forth.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Personalized News Recommendation in a Mobility Scenario Using Long-Short User Interest

Viana

Soares

2017

Int. J. Artif. Intell. Tools

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Access to information has been made easier in different domains that range from multimedia content, books, music, news, etc. To deal with the huge amount of alternatives, recommendation systems have been often used as a solution to filter the options and provide suggestions of items that might be of interest to an user. The news domain introduces additional challenges due not only to the large amount of new items produced daily but also due to their ephemeral timelife. In this paper, a news recommendation system which combines content-based and georeferenced techniques in a mobility scenario, is proposed. Taking into account the volatility of the information, short-term and long-term user profiles are considered and implicitly built. Besides tracking users’ clicks, the system infers different levels of interest an article has by tracking and weighting each action in the system and in social networks. Impact of the different fields that make up a news is also taken into account by following the inverted pyramid model that assumes different levels of importance to each paragraph of the article. The solution was tested with a population of volunteers and results indicate that the quality of the recommendation approach is acknowledged by the users.

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“…Currently, highly rated internet sites as Amazon 1 , eBay 2 or YouTube 3 use recommender systems as part of their services. Beyond these examples, recommender systems also apply on numerous other domains including books [8], movies and TV programs [35], [36], music [17], [18], news articles [4] and so forth.…”

Section: Related Workmentioning

confidence: 99%

A hybrid recommendation system for news in a mobile environment

Viana

Soares

2016

Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

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Over the last few years consumption of news articles has shifted more and more from the written versions towards the web. Mobile devices, which became more powerful, with larger screens and connected to the Internet, have had a great influence on this paradigm change. A critical problem associated to online news is related to the fact that the large number of daily articles can be overwhelming to the users. Recommendation services can largely improve the efficiency and accuracy of acquired information. These systems are designed to filter critical news, key events and meaningful items that might be of interest to a reader.In this paper, a news recommendation system in a mobility scenario is presented. The implemented recommendation system combines content-based and georeferenced recommendation techniques. Recommendations are supported by short-term and long-term user profiles created implicitly and considering also the mobile device geolocation. The final recommendation list is obtained by combining recommendations provided by the different recommendation approaches. To evaluate the performance of the solution, a user study was conducted. Results indicate that the quality of the recommendations is acknowledged by the test users. The system was integrated in a mobile application of a Portuguese newspaper (Público) in the context of the project Pglobal. CCS Concepts• Information systems ➝ Clustering • Information systems ➝ Content ranking • Information systems ➝ Information retrieval • Information systems ➝ Information extraction • Information systems ➝ Geographic information systems • Human-centered computing ➝ User studies

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TV Recommendation and Personalization Systems: Integrating Broadcast and Video On demand Services

Cited by 16 publications

References 7 publications

A Novel Rule based Data Mining Approach towards Movie Recommender System

A Novel Rule based Data Mining Approach towards Movie Recommender System

A Hybrid Approach for Personalized News Recommendation in a Mobility Scenario Using Long-Short User Interest

A hybrid recommendation system for news in a mobile environment

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