Master thesis abstract and presentation

Abstract: User modeling assists us to predict users’ behavior and interaction. Originated user model from a user can be used in a personalized system which user is interacting with it, for example, using to improve recommender systems. “Cold Start” is one of the principal challenges in user modeling, personalization and recommender systems which exists in all inner-system user modeling. This phenomenon causes sparse data on initial user data, which leads to an inaccurate forecast of the user’s behavior and then incorrect personalization and unsuitable recommendations. To overtake this problem, it is possible to use users’ public profile on other social media accounts of his or hers. This approach is the definition of cross-system modeling. The problem we are trying to solve in this study is retrieving metadata from the user’s public profile, which are presented on YouTube and Twitter in order to cause improvement in recommender systems personalization. That being said, Application Programming Interface or API has been employed to mine the data, and 5000 YouTube social media records have been recorded. Structure of the mined data has been reviewed and analyzed to discard outdated and outlier data. In order to review the connection between user’s features in two systems, Regression algorithm has been examined for precision and runtime execution measures. Results showed that subscribers count of a channel has little to none relation to count of uploaded videos of that channel. Also, connection and the same advantage of Twitter followers count feature of a person to predict YouTube total view count on the user’s channel has been concluded. The outcome of this study can be applied in the improvement of the personalized recommender system in YouTube channels where they have begun freshly. In these circumstances, it is feasible to use the Twitter follower count feature alternatively to the YouTube subscriber count feature to moderate the cold start problem for that channel.

Keywords: Recommender systems, user modeling, social media, cross-system user modeling, personalization