| With the astronomical growth of Web 2:0 over the past decade, social media systems, such as rating systems, social tagging systems, online forums, and community-based question answering (Q&A) systems, have revolutionized people's way of creating and sharing contents on the Web. However, due to the explosive growth of data in social media systems, users are drowning in information and encountering information overload problem. Currently, social computing techniques, achieved through learning with social media, have emerged as an important research area to help social media users find their information needs. In general, users post contents which reflect their interests in social media systems, and expect to obtain the suitable items through social computing techniques. To better understand users' interests, it is very essential to analyze different types of user generate content. On the other hand, the returned information may be items, or users with similar interests. Beyond the user-based analysis, it would be quite interesting and important to conduct item-oriented study, such as understand items' characteristics, and grouping items that are semantically related for better addressing users' information needs.;The objective of this thesis is to establish automatic and scalable models to help social media users find their information needs more effectively. These models are proposed based on the two key entities in social media systems: user and item. Thus, one important aspect of this thesis is therefore to develop a framework to combine the user information and the item information with the following two purposes: 1) modeling users' interests with respect to their behavior, and recommending items or users they may be interested in; and 2) understanding items' characteristics, and grouping items that are semantically related for better addressing users' information needs.;For the first purpose, a novel unified matrix factorization framework which fuses different types of users' behavior data, is proposed for predicting users' interests on new items. The framework tackles the data sparsity problem and non-flexibility problem confronted by traditional algorithms. Furthermore, to provide users with an automatic and effective way to discover other users with common interests, we propose a framework for user interest modeling and interest-based user recommendation by utilizing users' tagging information. Extensive evaluations on real world data demonstrate the effectiveness of the proposed user-based models.;For the second purpose, a new functionality question suggestion, which targets at suggesting questions that are semantically related to a queried question, is proposed in social media systems with Q&A functionalities. Existing bag-of-words approaches suffer from the shortcoming that they could not bridge the lexical chasm between semantically related questions. Therefore, we present two models which combines both the lexical and latent semantic knowledge to measure the semantic relatedness among questions. In question analysis, there is a lack of understanding of questions' characteristics. To tackle this problem, a supervised approach is developed to identify questions' subjectivity. Moreover, we come up with an approach to collect training data automatically by utilizing social signals without involving any manual labeling. The experimental results show that our methods perform better than the state-of-theart approaches.;In summary, based on the two key entities in social media systems, we present two user-based models and two item-oriented models to help social media users find their information needs more accurately and effectively through learning with social media. Extensive experiments on various social media systems confirm the effectiveness of proposed models. |
