A User-Centered Concept Mining System for Query and Document Understanding at Tencent

Concepts embody the knowledge of the world and facilitate the cognitive processes of human beings. Mining concepts from web documents and constructing the corresponding taxonomy are core research problems in text understanding and support many downstream tasks such as query analysis, knowledge base construction, recommendation, and search. However, we argue that most prior studies extract formal and overly general concepts from Wikipedia or static web pages, which are not representing the user perspective. In this paper, we describe our experience of implementing and deploying ConcepT in Tencent QQ Browser. It discovers user-centered concepts at the right granularity conforming to user interests, by mining a large amount of user queries and interactive search click logs. The extracted concepts have the proper granularity, are consistent with user language styles and are dynamically updated. We further present our techniques to tag documents with user-centered concepts and to construct a topic-concept-instance taxonomy, which has helped to improve search as well as news feeds recommendation in Tencent QQ Browser. We performed extensive offline evaluation to demonstrate that our approach could extract concepts of higher quality compared to several other existing methods. Our system has been deployed in Tencent QQ Browser. Results from online A/B testing involving a large number of real users suggest that the Impression Efficiency of feeds users increased by 6.01% after incorporating the user-centered concepts into the recommendation framework of Tencent QQ Browser.Comment: Accepted by KDD 201

The Evolving Landscape of Internet Control

Over the past two years, we have undertaken several studies at the Berkman Center designed to better understand the control of the Internet in less open societies. During the years we've been engaged in this research, we have seen many incidents that have highlighted the continuing role of the Internet as a battleground for political control, including partial or total Internet shutdowns in China, Iran, Egypt, Libya, and Syria; many hundreds of documented DDoS, hacking, and other cyber attacks against political sites; continued growth in the number of countries that filter the Internet; and dozens of well documented cases of on- and offline persecution of online dissidents. The energy dedicated to these battles for control of the Internet on both the government and dissident sides indicated, if nothing else, that both sides think that the Internet is a critical space for political action. In this paper, we offer an overview of our research in the context of these changes in the methods used to control online speech, and some thoughts on the challenges to online speech in the immediate future

Youth and Digital Media: From Credibility to Information Quality

Building upon a process-and context-oriented information quality framework, this paper seeks to map and explore what we know about the ways in which young users of age 18 and under search for information online, how they evaluate information, and how their related practices of content creation, levels of new literacies, general digital media usage, and social patterns affect these activities. A review of selected literature at the intersection of digital media, youth, and information quality -- primarily works from library and information science, sociology, education, and selected ethnographic studies -- reveals patterns in youth's information-seeking behavior, but also highlights the importance of contextual and demographic factors both for search and evaluation. Looking at the phenomenon from an information-learning and educational perspective, the literature shows that youth develop competencies for personal goals that sometimes do not transfer to school, and are sometimes not appropriate for school. Thus far, educational initiatives to educate youth about search, evaluation, or creation have depended greatly on the local circumstances for their success or failure

Counterfactual Estimation and Optimization of Click Metrics for Search Engines

Optimizing an interactive system against a predefined online metric is particularly challenging, when the metric is computed from user feedback such as clicks and payments. The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different search result page for the same query, but we normally cannot infer reliably from search log how users would react to the new result page. Consequently, it appears impossible to accurately estimate online metrics that depend on user feedback, unless the new engine is run to serve users and compared with a baseline in an A/B test. This approach, while valid and successful, is unfortunately expensive and time-consuming. In this paper, we propose to address this problem using causal inference techniques, under the contextual-bandit framework. This approach effectively allows one to run (potentially infinitely) many A/B tests offline from search log, making it possible to estimate and optimize online metrics quickly and inexpensively. Focusing on an important component in a commercial search engine, we show how these ideas can be instantiated and applied, and obtain very promising results that suggest the wide applicability of these techniques