Items where author is affiliated with Microsoft Research Cambridge
Number of items: 2.
and Vojnovic, Milan Behavioral Profiles for Advanced Email Features.
We examine the behavioral patterns of email usage in a large-scale enterprise over a three-month period. In particular, we focus on two main questions: (Q1) what do replies depend on? and (Q2) what is the gain of augmenting contacts through the friends of friends from the email social graph? For Q1, we identify and evaluate the signiﬁcance of several factors that affect the reply probability and the email response time. We ﬁnd that all factors of our considered set are signiﬁcant, provide their relative ordering, and identify the recipient list size, and the intensity of email communication between the correspondents as the dominant factors. We highlight various novel threshold behaviors and provide support for existing hypotheses such as that of the least-effort reply. For Q2, we ﬁnd that the number of new contacts extracted from the friends-of-friends relationships amounts to a large number, but which is still a limited portion of the total enterprise size. We believe that our results provide signiﬁcant insights towards informed design of advanced email features, including those of social-networking type. Categories & Subject Descriptors: H.4.3 [Communications Applications]: Electronic mail General Terms: Design, Measurement, Human Factors Keywords: Reply time, reply probability, email proﬁles.
and Liu, Chao
and Kannan, Anitha
and Minka, Tom
and Taylor, Michael
and Wang, Yi-Min
and Faloutsos, Christos Click Chain Model in Web Search.
Given a terabyte click log, can we build an efficient and effective click model? It is commonly believed that web search click logs are a gold mine for search business, because they reﬂect users’ preference over web documents presented by the search engine. Click models provide a principled approach to inferring user-perceived relevance of web documents, which can be leveraged in numerous applications in search businesses. Due to the huge volume of click data, scalability is a must. We present the click chain model (CCM), which is based on a solid, Bayesian framework. It is both scalable and incremental, perfectly meeting the computational challenges imposed by the voluminous click logs that constantly grow. We conduct an extensive experimental study on a data set containing 8.8 million query sessions obtained in July 2008 from a commercial search engine. CCM consistently outperforms two state-of-the-art competitors in a number of metrics, with over 9.7% better log-likelihood, over 6.2% better click perplexity and much more robust (up to 30%) prediction of the ﬁrst and the last clicked position.
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