Personalized TV Contents Recommender System Using Collaborative Context tagging-based User’s Preference Prediction Technique

Lee, Haesung; Kwon, Joonhee

doi:10.14257/ijmue.2014.9.5.23

Cited by 8 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Collaborative filtering systems compute profile similarity between the other users and the target user "by comparing users' opinions of items." Profile similarity is usually computed by comparing rating-vectors with various distance metrics with user correlation or cosine similarity [20].…”

Section: Recommendation Systemsmentioning

confidence: 99%

Bayesian Model-based Personalized Recommendation Service

Kim¹,

Kim²,

Kim³

2017

IJUNESST

View full text Add to dashboard Cite

show abstract

Section: Recommendation Systemsmentioning

confidence: 99%

Bayesian Model-based Personalized Recommendation Service

Kim¹,

Kim²,

Kim³

2017

IJUNESST

View full text Add to dashboard Cite

show abstract

“…A few recommendation systems have been designed for digital television. Collaborative context tagging-based prediction of user preferences is described in [1]. However, we would like to use content information about program already available by broadcast service provider via electronic program guide.…”

Section: Related Workmentioning

confidence: 99%

Recommendation system for HBB TV: Model design and implementation

Posoldova

Liew

2015

IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON)

View full text Add to dashboard Cite

Hybrid broadcast and broadband (HBB) television is a new platform that opens many possibilities for new services. Recommendation system offers a personalized service that suggests items of interest according to user preference. Nowadays, the number of available programs is so large that one cannot realistically have a real time overview. Recommendation engines were developed to solve the problem of information overload, and save time and effort when looking for appealing content. In this paper, we present model design and implementation of a recommendation system for HBB TV. To explore user preferences and make predictions, an enhanced Naïve Bayes model for rating prediction is designed. The model uses a set of features to predict user rating based on past observation. The recommendation system presented in this paper is flexible and robust enough to handle a sparse data set with very few records of feature description. Experiments performed on a Yahoo movie data set indicated the promising performance of our approach.

show abstract