Recently, a number of algorithms have been proposed to obtain hierarchical structures — so-called folksonomies — from social tagging data. Work on these algorithms is in part ...
Denis Helic, Markus Strohmaier, Christoph Trattner...
Existing enterprise calendaring systems have suffered from problems like rigidity, lack of transparency, and poor integration with social networks. We present the system design an...
Mikhil Masli, Werner Geyer, Casey Dugan, Beth Brow...
We investigate the problem of learning to rank for document retrieval from the perspective of learning with multiple objective functions. We present solutions to two open problems...
Krysta Marie Svore, Maksims Volkovs, Christopher J...
Query auto completion is known to provide poor predictions of the user’s query when her input prefix is very short (e.g., one or two characters). In this paper we show that con...
In this work, we study the notion of competing campaigns in a social network. By modeling the spread of influence in the presence of competing campaigns, we provide necessary too...
Measuring the causal effects of online advertising (adfx) on user behavior is important to the health of the WWW publishing industry. In this paper, using three controlled experi...
Gradient Boosted Regression Trees (GBRT) are the current state-of-the-art learning paradigm for machine learned websearch ranking — a domain notorious for very large data sets. ...
Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal...
Social hierarchy and stratification among humans is a well studied concept in sociology. The popularity of online social networks presents an opportunity to study social hierarch...
Mangesh Gupte, Pravin Shankar, Jing Li, S. Muthukr...
We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter...