Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques. In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.

A better understanding of strategies and behavior of successful searchers is crucial for improving the experience of all searchers. However, research of search behavior has been struggling with the tension between the relatively small-scale, but controlled lab studies, and the large-scale log-based studies where the searcher intent and many other important factors have to be inferred. We present our solution for performing controlled, yet realistic, scalable, and reproducible studies of searcher behavior. We focus on difficult informational tasks, which tend to frustrate many users of the current web search technology. First, we propose a principled formalization of different types of "success" for informational search, which encapsulate and sharpen previously proposed models. Second, we present a scalable game-like infrastructure for crowdsourcing search behavior studies, specifically targeted towards capturing and evaluating successful search strategies on informational tasks with known intent. Third, we report our analysis of search success using these data, which confirm and extends previous findings. Finally, we demonstrate that our model can predict search success more effectively than the existing state-of-the-art methods, on both our data and on a different set of log data collected from regular search engine sessions. Together, our search success models, the data collection infrastructure, and the associated behavior analysis techniques, significantly advance the study of success in web search.

A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the user’s preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the user’s interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing-based recommender system:

The motivation behind many Information Retrieval systems is to identify and present relevant information to people given their current goals and needs. Learning about user preferences and access patterns recent technologies make it possible to model user information needs and adapt services to meet these needs. In previous work we have presented ASSIST, a general-purpose platform which incorporates various types of social support into existing information access systems and reported on our deployment experience in a highly goal driven environment (ACM Digital Library). In this work we present our experiences in applying ASSIST to a domain where goals are less focused and where casual exploration is more dominant; YouTube. We present a general study of YouTube access patterns and detail how the ASSIST architecture affected the access patterns of users in this domain.