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Open Access Publications from the University of California

Exploring Temporal Context for Collaborative Filtering

  • Author(s): Elsisy, Amr
  • Advisor(s): Papalexakis, Vagelis
  • et al.
Abstract

Thousands of new users join social media website everyday, generating huge amounts of new data. Twitter users for example, generate millions of new posts per day. This can flood our users with huge amounts of information, and thus overload them with information that for the most part they are not interested in. To fix this problem, we need to only show our users information relative to them, such as posts from people they are following. This thesis focuses on how to make accurate recommendations to each user, on which users/pages to follow, thus helping the user view information that is important to them. In particular, we focus on exploring the following research questions: 1) which features yield the best recommendation accuracy, and 2) given those features, what is the best granularity for them, that captures the underlying dynamics, leading to high accuracy.

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