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In this section, a scenario is given to explain how the proposed VCSR system recommends new videos to users and what kind of role the system plays.
Using the proposed VCSR system, all of the above problems can be turned into an automated process.
The system architecture of the VCSR system is illustrated in Figure 3.
A comparison of the differences between the proposed VCSR system and other video recommendation systems is shown in Table 1.
The purpose of the experiment is to examine whether or not the VCSR system can motivate learners to watch videos.
Emails from Phase 2 proved to be more useful in helping learners assimilate knowledge than the emails from Phase 1, owing to the versatility of the VCSR system.
The VCSR system can extract a summary from numerous unstructured data (raw video), in turn constructing a recommendation.
In this paper, we presented an automatic multimedia content summarization and adaptable recommendation system, called VCSR, which is able to auto-recommend suitable video content for learners.
In addition, we are going to create a forum where learners can give their feedback and discuss issues relating to the VCSR system.
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