An automatic multimedia content summarization system for video recommendation

In order to help learners choose videos that are suitable for specific learning activities, two issues must be addressed: (a) the appropriate summarization technique must be able to extract semantic information from video content; and (b) the appropriate system must be able to recommend a suitable video to learners from a huge database. The research regarding both of the above points is very limited, and few attempts have been made to apply summarized information to recommendation mechanisms. For example, MovieLens is a well-known movie recommendation website (http://movielens.umn.edu). It generates personalized recommendations on the basis of a user preference. Nevertheless, the recommendation information provided by the site lacks an integral plot summary. Therefore, users might not be able to browse a sufficient amount of information in order to determine whether the movie is related to the knowledge that interests them. YouTube (http://www.youtube.com/), while employing a different system, still falls short with regard to providing videos that are appropriate for specific learning activities. This famous online video streaming service allows anyone to view and share videos that have been uploaded by others. Users can get videos by searching keywords on the website. Unfortunately, users will likely spend a lot of time looking for related videos through the search mechanism rather than receiving relevant information from the recommendation mechanism. Due to such situations, attaining knowledge efficiently through videos may prove to be a difficult task that could even lead to a decrease in motivation on the part of the learner.

Motivation is an important factor for learning. The ARCS model of motivation was formed in response to the necessity of finding more useful ways of understanding the major factors relating to the motivation to learn (Keller, 1983; Keller & Kopp, 1987). This model identifies four major factors: attention, relevance, confidence and satisfaction. All of these factors must be fulfilled if a learner is to become and remain motivated (Dick, Carey & Carey, 2001). Based on the effectiveness of multimedia learning, we hope to develop a video recommendation system that attracts the learner's attention, recommends relevant videos, and effectively promotes learner confidence and satisfaction.

It is clear that multimedia learning is useful for learners, but there is not a customized tool or mechanism for multimedia learning that perpetuates learner motivation. In this paper, we extend our previous works (Huang, Tsai, Chung, Shen, Yang & Wu, 2007; Tsai, Chung, Huang, Shen, Wu & Yang, 2007) and present an automatic multimedia content summarization and adaptable recommendation system, called Video Content Summarization for Recommendation (VCSR), that auto-recommends suitable multimedia material with the aim of encouraging learners to watch and assimilate knowledge within the framework. The proposed system first extracts video content as a summary and collects corresponding frames from the source. These materials are combined into a hypermedia document and auto-recommended to learners. The system also sends the hypermedia document as email (multimedia-based email) to learners in response to their profiles. Unlike traditional recommendation methods, the system not only recommends video titles, but also includes important extracted content that contains a video summary and corresponding video clips. The system can extract information rapidly from a large database of videos, saving time for the user. Moreover, the system can recommend video material to learners related to what they wish to study. Thus, learners can quickly use the new video information acquired instead of receiving a lot of unnecessary information.