Extracting viewer interests for automated bookmarking in video-on-demand services

Abstract

Video-on-demand (VoD) services have become popular on the Internet in recent years. In VoD, it is challenging to support the VCR functionality, especially the jumps, while maintaining a smooth streaming quality. Previous studies propose to solve this problem by predicting the jump target locations and prefetching the contents. However, through our analysis on traces from a real-world VoD service, we find that it would be fundamentally difficult to improve a viewer's VCR experience by simply predicting his future jumps, while ignoring the intentions behind these jumps.Instead of the prediction-based approach, in this paper, we seek to support the VCR functionality by bookmarking the videos. There are two key techniques in our proposed methodology. First, we infer and differentiate viewers' intentions in VCR jumps by decomposing the interseek times, using an expectation-maximization (EM) algorithm, and combine the decomposed inter-seek times with the VCR jumps to compute a numerical interest score for each video segment. Second, based on the interest scores, we propose an automated video bookmarking algorithm. The algorithm employs the time-series change detection techniques of CUSUM and MB-GT, and bookmarks videos by detecting the abrupt changes on their interest score sequences. We evaluate our proposed techniques using real-world VoD traces from dozens of videos. Experimental results suggest that with our methods, viewers' interests within a video can be precisely extracted, and we can position bookmarks on the video's highlight events accurately. Our proposed video bookmarking methodology does not require any knowledge on video type, contents, and semantics, and can be applied on various types of videos.

A Video Timeline with Bookmarks and Prefetch State for Faster Video Browsing

“…Figure 1(b) shows an example. These bookmarks can be determined manually by content provider, or automatically through content analysis or user behavior analysis [3,19], to identify popular or semantically important points in the video content. Bookmarks can be annotated with text describing the content or images that depict the content of the videos (as shown in Figure 1(b)).…”