Academic Journal
Broadcast Content Recommender System based on User's Viewing History
| Title: | Broadcast Content Recommender System based on User's Viewing History |
|---|---|
| Authors: | Soo-Young Oh, Yeon-Hee Oh, Sung-Hee Han, Hee-Jung Kim |
| Source: | Journal of Broadcast Engineering. 17:129-139 |
| Publisher Information: | The Korean Institute of Broadcast and Media Engineers, 2012. |
| Publication Year: | 2012 |
| Subject Terms: | 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 12. Responsible consumption |
| Description: | 01234 -5 (64789:; ? -@. =AB, C, DE5F=@G H: .IJK9L, M)NOPQRST P:UV@. NOP3WX?5YZU5[:\]> .Z^ZU5 .Zcdee5f:@G .Z=mnop:BYlZ@. Fq35rsRtu, 012RB=NOPQRST P:jk4vNZW4-@. 35 (64wZ Wx5yA 4AzZ^4`{:>_|-@. x5yA =}~^N>Z W -@. 3, H4Z L, N 5 > ZW4g H:#-@. #pg3RB_|Z=API4;B IL, API=HTTP [:`> 9: I@. c9: Q>k-4? -@.AbstractThis paper introduces a recommender system that is to recommend broadcast content. Our recommender system uses user’s viewing history for personalized recommendations. Broadcast contents has unique characteristics as compared with books, musics and movies. There are two types of broadcast content, a series program and an episode program. The series program is comprised of several programs that deal with the same topic or story. Meanwhile, the episode program covers a variety of topics. Each program of those has different topic in general. Therefore, our recommender system recommends TV programs to users according to the type of broadcast content. The recommendations in this system are based on user’s viewing history that is used to calculate content similarity between contents. Content similarity is calculated by exploiting collaborative filtering algorithm. Our recommender system uses java sparse array structure and performs memory-based processing. And then the results of processing are stored as an index structure. Our recommender system provides recommendation items through OPEN APIs that utilize the HTTP Protocol. Finally, this paper introduces the implementation of our recommender system and our web demo.Keyword : broadcast content, recommender system, collaborative filtering, recommendation, tv program%12 http://dx.doi.org/10.5909/JEB.2012.17.1.129 |
| Document Type: | Article Other literature type |
| Language: | English |
| ISSN: | 1226-7953 |
| DOI: | 10.5909/jeb.2012.17.1.129 |
| Access URL: | http://www.ndsl.kr/soc_img/society/ksbe/BSGHC3/2012/v17n1/BSGHC3_2012_v17n1_129.pdf |
| Accession Number: | edsair.doi.dedup.....840be752d3c9dd34cd6a209355f8656c |
| Database: | OpenAIRE |
| ISSN: | 12267953 |
|---|---|
| DOI: | 10.5909/jeb.2012.17.1.129 |