International Journal of Multimedia Data Engineering & Management
A Multimedia Recommender System
ACM Transactions on Internet Technology (TOIT)
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Social network studies are becoming increasingly popular and have been applied to several fields of study such as law enforcement, marketing, spread of disease, as well as in the improvement of organizational performance. One area that is yet to be explored relates to harnessing the power of social networks as recommender systems. The idea that users may provide other users with recommendations that are more relevant than naive approaches is long known. However, the approaches currently implemented are based on the creation of simple relationships such as co-purchase of similar items. Similarly, video websites would like to suggest related videos to users to maximize the time they spend on their sites. Ergo it is crucial that sites like YouTube provide users with recommendations that are relevant to them. Moreover, given the large amount of videos on YouTube, a good recommender system may alleviate users' efforts on finding videos that interest them the most. Existing recommender systems for YouTube are typically based on finding similarities between the videos' textual features (video title or tags annotations) and matching these features to tags in the user's profiles or the videos they are currently watching. This approach is very limited because it restricts the users interests to the current theme of the video being played or the preferences in their profiles. This means that if one is watching a movie about football, it is assumed that the user would like recommendations on other videos about football. In this paper, we attempt to extract information about video relationship using a network formed from reviews left as comments in YouTube videos. We create a network of videos called YouTube Recommender Network (YRN) and use complex network analysis on this network as the basis of a recommender system. Our results show that our list of recommended videos is more diverse than the ones based on textual information. Our YRN provides diversity and captures other important characteristics such as high rating and most-viewed count.