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Cross-System Social Web User Modeling Personalization of Recommender Systems


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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 Structure of the mined data has been reviewed and analyzed to discard outdated and outlier data.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.

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Cross-System Social Web User Modeling Personalization of Recommender Systems

  1. 1. Cross-System Social Web User Modeling Personalization of Recommender Systems Amirmasood Sheidayi Supervisor: Dr. Elaheh Homayounvala
  2. 2. Introduction  What is Recommender System?  What is personalization, and what does personalized recommender systems mean?  How does user modeling define?  What is cross-system user modeling?
  3. 3. The cold start problem in isolated user modelling  cold start causes by sparse data at initialization that heads to an incorrect prediction from user behavior and so untrue personalization and recommendations. user modeling behavior prediction Personali- zation Improve RS cold start problem
  4. 4. Cross-system user modeling  Use other social media data  Access to public data of users  Fetch implicit, explicit and inferable data  Aggregate social media data
  5. 5. Overview of actions  Use the most popular social network platforms  Alleviate the cold start problem  Introducing a new feature for mitigating the cold start problem  Create a dataset select features prediction cold start collect data
  6. 6. Actions of creating the dataset prediction improve personalization of recommender systems data restoration 5k source data aggregation speed and accuracy mine data
  7. 7. Final dataset  300 common final record  Select a limited number of features from the dataset
  8. 8. Sample fetched of dataset feature YouTube Twitter subscriber count follower count list of uploaded videos list of followers captions of uploaded videos followings count thumbnails of uploaded videos list of followings likes/dislikes of a video list of tweets about section of a channel list of people who liked a tweet list of links in the about section list of people who replied to a tweet joined date join date of a user total view count birthday of a user total uploaded video count list of tweets which user has liked location list of media(picture/videos) which user has published
  9. 9. Challenges of collecting data  Due to privacy policy, there is not data with actual person specification (e.g., Name, E-mail, etc.)  Different social media API restrictions  No feasibility of using questionnaire  The time-consuming task of gathering and restoring data
  10. 10. YouTube data - Video Count - Subscriber Count - Video Count - Hidden Subscriber Count Response Status: - OK - Fail
  11. 11. Aggregating data Word Cloud of Links on About page of YouTube Channels Social Media Links Distribution 1. Twitter 2. Facebook 3. Instagram 4. Other
  12. 12. Selecting Features  Twitter followers  YouTube subscriber count  Total YouTube view count  The difference of view count and a subscriber count of YouTube  Total uploaded video count of YouTube
  13. 13. Feature Correlation Heat chart  The close connection between subscriber count and total view count on YouTube  Near no connection between uploaded video count and view count on a YouTube Channel  Same importance between Twitter follower count and YouTube subscriber count
  14. 14. Output Prediction via Regression Algorithm  Predication on view count  Average view count of 3,550,524,704.7  Max Residual Error at 104.61  Mean Absolute Error at 27.27  Mean Running Time 372 milliseconds
  15. 15. Outcome  Tolerable time and precision of the regression algorithm  Ability to use results in flat and stereotype user modeling  Near no connection between uploaded video count and view count on a YouTube Channel
  16. 16. Outcome  Ability to utilize personalized recommender systems on freshly begun YouTube channels  Capability to substitute Twitter follower count instead of subscriber count for freshly began YouTube channels to alleviate cold start problem Twitter Followers YouTube Subscribers Prediction of total YouTube view count
  17. 17. Suggestions  Check the content of images, videos, and texts of Tweets and videos  Check YouTube links in a Tweet  Check based on YouTube channel classification  Check videos and tweets head to head  Add other common social media  Creating a system for collecting users' public data for application on other social media
  18. 18. Thanks.