Michelle X. Zhou

TIST Journal 2016 Journal Article

Introduction to the Special Issue on Recommender System Benchmarking

• Paolo Cremonesi
• Alan Said
• Domonkos Tikk
• Michelle X. Zhou

TIST Journal 2013 Journal Article

Introduction to the special section on social recommender systems

• Ido Guy
• Li Chen
• Michelle X. Zhou

TIST Journal 2012 Journal Article

TIARA

• Shixia Liu
• Michelle X. Zhou
• Shimei Pan
• Yangqiu Song
• Weihong Qian
• Weijia Cai
• Xiaoxiao Lian

We are building an interactive visual text analysis tool that aids users in analyzing large collections of text. Unlike existing work in visual text analytics, which focuses either on developing sophisticated text analytic techniques or inventing novel text visualization metaphors, ours tightly integrates state-of-the-art text analytics with interactive visualization to maximize the value of both. In this article, we present our work from two aspects. We first introduce an enhanced, LDA-based topic analysis technique that automatically derives a set of topics to summarize a collection of documents and their content evolution over time. To help users understand the complex summarization results produced by our topic analysis technique, we then present the design and development of a time-based visualization of the results. Furthermore, we provide users with a set of rich interaction tools that help them further interpret the visualized results in context and examine the text collection from multiple perspectives. As a result, our work offers three unique contributions. First, we present an enhanced topic modeling technique to provide users with a time-sensitive and more meaningful text summary. Second, we develop an effective visual metaphor to transform abstract and often complex text summarization results into a comprehensible visual representation. Third, we offer users flexible visual interaction tools as alternatives to compensate for the deficiencies of current text summarization techniques. We have applied our work to a number of text corpora and our evaluation shows promise, especially in support of complex text analyses.

TIST Journal 2011 Journal Article

Who is Doing What and When

• Shiwan Zhao
• Michelle X. Zhou
• Xiatian Zhang
• Quan Yuan
• Wentao Zheng
• Rongyao Fu

Content-centric social Web sites, such as discussion forums and blog sites, have flourished during the past several years. These sites often contain overwhelming amounts of information that are also being updated rapidly. To help users locate their interests at such sites (e.g., interesting blogs to read or discussion forums to join), researchers have developed a number of recommendation technologies. However, it is difficult to make effective recommendations for new users (a.k.a. the cold start problem) due to a lack of user information (e.g., preferences and interests). Furthermore, the complexity of recommendation algorithms often prevents users from comprehending let alone trusting the recommended results. To tackle these above two challenges, we are building a social map-based recommender system called Pharos. A social map summarizes users’ content-related social behavior over time (e.g., reading, writing, and commenting behavior during the past week) as a set of latent communities. For a given time interval, each community is characterized by the theme of the content being discussed and the key people involved. By discovering, ranking, and displaying the most popular latent communities at different time intervals, Pharos creates a time-sensitive, visual social map of a Web site. This enables new users to obtain a quick overview of the site, alleviating the cold start problem. Furthermore, we use the social map as a context to help explain Pharos-recommended content and people. Users can also interactively explore the social map to locate the content in which they are interested or people that are not being explicitly recommended, compensating for the imperfections in the recommendation algorithms. We have developed several Pharos applications, one of which is deployed within our company. Our preliminary evaluation of the deployed application shows the usefulness of Pharos.

IJCAI Conference 2003 Conference Paper

Automated Generation of Graphic Sketches by Example

• Michelle X. Zhou
• Min Chen

Hand-crafting effective visual presentations is time-consuming and requires design skills. Here we present a case-based graphic sketch generation algorithm, which uses a database of existing graphic examples (cases) to automatically create a sketch of a presentation for a new user request. As the first case-based learning approach to graphics generation, our work offers three unique contributions. First, we augment a similarity metric with a set of adequacy evaluation criteria to retrieve a case that is most similar to the request and is also usable in sketch synthesis. To facilitate the retrieval of case fragments, we develop a systematic approach to case/request decomposition when a usable case cannot be found. Second, we improve case retrieval speed by organizing cases into hierarchical clusters based on their similarity distances and by using dynamically selected cluster representatives. Third, we develop a general case composition method to synthesize a new sketch from multiple retrieved cases. Furthermore, we have implemented our casebased sketch generation algorithm in a user-system cooperative graphics design system called IMPRO- VISE-! -, which helps users to generate creative and tailored presentations.

IJCAI Conference 1997 Conference Paper

The Representation and Use of a Visual Lexicon for Automated Graphics Generation

• Michelle X. Zhou
• Steven K. Feiner