Physical Sciences and Mathematics Commons^™

A Bayesian Recommender Model For User Rating And Review Profiling, Mingming Jiang, Dandan Song, Lejian Liao, Feida Zhu

Research Collection School Of Computing and Information Systems

Intuitively, not only do ratings include abundant information for learning user preferences, but also reviews accompanied by ratings. However, most existing recommender systems take rating scores for granted and discard the wealth of information in accompanying reviews. In this paper, in order to exploit user profiles' information embedded in both ratings and reviews exhaustively, we propose a Bayesian model that links a traditional Collaborative Filtering (CF) technique with a topic model seamlessly. By employing a topic model with the review text and aligning user review topics with "user attitudes" (i.e., abstract rating patterns) over the same distribution, our method achieves …

Event Identification And Analysis On Twitter, Qiming Diao

Dissertations and Theses Collection (Open Access)

With the rapid growth of social media, Twitter has become one of the most widely adopted platforms for people to post short and instant messages. Because of such wide adoption of Twitter, events like breaking news and release of popular videos can easily capture people’s attention and spread rapidly on Twitter. Therefore, the popularity and importance of an event can be approximately gauged by the volume of tweets covering the event. Moreover, the relevant tweets also reflect the public’s opinions and reactions to events. It is therefore very important to identify and analyze the events on Twitter. In this dissertation, …

Information Filtering By Multiple Examples, Mingzhu Zhu

Dissertations

A key to successfully satisfy an information need lies in how users express it using keywords as queries. However, for many users, expressing their information needs using keywords is difficult, especially when the information need is complex. Search By Multiple Examples (SBME), a promising method for overcoming this problem, allows users to specify their information needs as a set of relevant documents rather than as a set of keywords.

Most of the studies on SBME adopt the Positive Unlabeled learning (PU learning) techniques by treating the user's provided examples (denoted as query examples) as positive set and the entire data …

Mining User Viewpoints In Online Discussions, Minghui Qiu

Dissertations and Theses Collection (Open Access)

Online discussion forums are a type of social media which contains rich usercontributed facts, opinions, and user interactions on diverse topics. The large volume of opinionated data generated in online discussions provides an ideal testbed for user opinion mining. In particular, mining user opinions on social and political issues from online discussions is useful not only to government organizations and companies but also to social and political scientists. In this dissertation, we propose to study the task of mining user viewpoints or stances from online discussions on social and political issues. Specifically, we will talk about our proposed approaches for …