Effective Cross Domain Recommendation for TV User
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064

Research Paper | Computer Science & Engineering | India | Volume 6 Issue 2, February 2017

Effective Cross Domain Recommendation for TV User

Sumathy.R, Suguna. T, Sangeetha M.

Social TV is a social media service via TV and social network through which TV users exchange their experiences about TV programs that they are viewing. For social TV service, two technical aspects are envisioned grouping of similar TV users to make social TV communities and recommending TV programs based on group and individual interests for personalizing TV. In this, we propose a unified topic model based on grouping of related TV users and recommending TV programs as a social TV service. The proposed unified topic model employs two Latent Dirichlet Allocation (LDA) models.One is a topic model of TV users, and the other is topic model of the description words for viewed TV programs. The two LDA models are then integrated via a topic proportion parameter for TV programs. Our proposed unified topic model will be extended for cross-domain recommendation where three topic models of TV users viewing history data, TV program description data, and web content description data are tied together and an appropriate rank method for cross-domain recommendation is studied. Using the extended unified topic model, effective cross-domain recommendation is expected to be feasible from the TV domain to the web domain for TV users who can then easily select associated web contents for the TV programs that they have enjoyed watching. With the help of unified model we identifies the semantic relation between TV user groups and TV program description word groups so that more meaningful TV program recommendations can be made. The unified topic model also overwhelms an item ramp-up problem such that new TV programs can be reliably recommended to TV users. Furthermore, from the topic model of TV users, TV users with related tastes can be grouped as topics, which can then be recommended as social TV communities.

Keywords: LatentDirichlet Allocation LDA, modelparameter update, social TV, topic model, TV programrecommendation

Edition: Volume 6 Issue 2, February 2017

Pages: 2101 - 2104

Share this Article

How to Cite this Article?

Sumathy.R, Suguna. T, Sangeetha M., "Effective Cross Domain Recommendation for TV User", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART20171191, Volume 6 Issue 2, February 2017, 2101 - 2104

80 PDF Views | 64 PDF Downloads

Download Article PDF



Similar Articles with Keyword 'social TV'

Research Paper, Computer Science & Engineering, India, Volume 6 Issue 2, February 2017

Pages: 2101 - 2104

Effective Cross Domain Recommendation for TV User

Sumathy.R, Suguna. T, Sangeetha M.

Share this Article

Research Paper, Computer Science & Engineering, India, Volume 3 Issue 9, September 2014

Pages: 1129 - 1135

Mobile Cloud Computing Applied in Social TV

K Ramu, A Jyothi

Share this Article

M.Tech / M.E / PhD Thesis, Computer Science & Engineering, India, Volume 3 Issue 9, September 2014

Pages: 1568 - 1573

Mobile Social TV Interaction through Cloud Computing

Ambika, Ramya Bathula

Share this Article

Similar Articles with Keyword 'topic model'

Survey Paper, Computer Science & Engineering, India, Volume 4 Issue 10, October 2015

Pages: 1750 - 1751

Automatic Emotion Generation and Summarization form Perceptual Text ? A Survey

Sayalee Sandeep Raut, Kavita P. Shirsat

Share this Article

Survey Paper, Computer Science & Engineering, India, Volume 4 Issue 11, November 2015

Pages: 2095 - 2097

Trust Evaluation by Mining of E-commerce Feedback Comments

Priyanka Kumbhar, Manjushri Mahajan

Share this Article

Survey Paper, Computer Science & Engineering, India, Volume 5 Issue 7, July 2016

Pages: 1876 - 1878

A Survey on CommTrust: Computing Multi-Dimensional Trust by Mining E-Commerce Feedback Comments

Swetha Sam, Vani V Prakash

Share this Article

Research Paper, Computer Science & Engineering, India, Volume 6 Issue 2, February 2017

Pages: 2101 - 2104

Effective Cross Domain Recommendation for TV User

Sumathy.R, Suguna. T, Sangeetha M.

Share this Article

Survey Paper, Computer Science & Engineering, India, Volume 5 Issue 1, January 2016

Pages: 543 - 545

Survey on Seizure Reports from Clandestine Labs

P. Ramina, M. Vanitha

Share this Article
Top