Name
Abstract | ||
|---|---|---|
With the proliferation of online distribution methods for videos, content owners require easier and more effective methods for monetization through advertising. Matching advertisements with related content has a significant impact on the effectiveness of the ads, but current methods for selecting relevant advertising keywords for videos are limited by reliance on manually supplied metadata. In this paper we study the feasibility of using text available from video content to obtain high quality keywords suitable for matching advertisements. In particular, we tap into three sources of text for ad keyword generation: production scripts, closed captioning tracks, and speech-to-text transcripts. We address several challenges associated with using such data. To overcome the high error rates prevalent in automatic speech recognition and the lack of an explicit structure to provide hints about which keywords are most relevant, we use statistical and generative methods to identify dominant terms in the source text. To overcome the sparsity of the data and resulting vocabulary mismatches between source text and the advertiser's chosen keywords, these terms are then expanded into a set of related keywords using related term mining methods. Our evaluations present a comprehensive analysis of the relative performance for these methods across a range of videos, including professionally produced films and popular videos from YouTube. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1871437.1871637 | CIKM |
Keywords | Field | DocType |
related term mining method,video content,related content,relevant advertising keyword,source text,content owner,generating advertising keyword,high quality,matching advertisement,related keyword,high error rate,error rate,speech to text,advertising,automatic speech recognition | Metadata,Data mining,Closed captioning,Advertising,Information retrieval,Computer science,Monetization,Natural language processing,Artificial intelligence,Source text,Vocabulary,Scripting language | Conference |
Citations | PageRank | References |
1 | 0.36 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael J. Welch | 1 | 87 | 4.84 |
| Junghoo Cho | 2 | 3088 | 584.54 |
| Walter Chang | 3 | 251 | 159.67 |