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This paper is concerned with the prediction of clicking an ad in sponsored search. The accurate prediction of user's click on an ad plays an important role in sponsored search, because it is widely used in both ranking and pricing of the ads. Previous work on click prediction usually takes a single ad as input, and ignores its relationship to the other ads(More)
Graph ranking plays an important role in many applications, such as page ranking on web graphs and entity ranking on social networks. In applications, besides graph structure, rich information on nodes and edges and explicit or implicit human supervision are often available. In contrast, conventional algorithms (e.g., PageRank and HITS) compute ranking(More)
We propose a <i>General Markov Framework</i> for computing page importance. Under the framework, a <i>Markov Skeleton Process</i> is used to model the random walk conducted by the web surfer on a given graph. Page importance is then defined as the product of <i>page reachability</i> and <i>page utility</i>, which can be computed from the transition(More)
In this paper we present our design of a novel system, named nReader, to help people read online news. According to researches on news recommendation and a newly deployed survey on user's feeling and requirement about current news reading style, we build our system by adding extra feature to the framework of the popular RSS (Rich Site Summary) system.We(More)
Click prediction is one of the fundamental problems in sponsored search. Most of existing studies took advantage of machine learning approaches to predict ad click for each event of ad view independently. However, as observed in the real-world sponsored search system, user's behaviors on ads yield high dependency on how the user behaved along with the past(More)
Precise prediction of the probability that users click on ads plays a key role in sponsored search. State-of-the-art sponsored search systems typically employ a machine learning approach to conduct click prediction. While paying much attention to extracting useful features and building effective models, previous studies have overshadowed seemingly less(More)
Precise click prediction is one of the key components in the sponsored search system. Previous studies usually took advantage of two major kinds of information for click prediction, i.e., relevance information representing the similarity between ads and queries and historical click-through information representing users' previous preferences on the ads.(More)
This paper is concerned with Markov processes for computing page importance. Page importance is a key factor in Web search. Many algorithms such as PageRank and its variations have been proposed for computing the quantity in different scenarios, using different data sources, and with different assumptions. Then a question arises, as to whether these(More)