Learn More
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)
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)
Regularized empirical risk minimization (R-ERM) is an important branch of machine learning, since it constrains the capacity of the hypothesis space and guarantees the generalization ability of the learning algorithm. Two classic proximal optimization algorithms, i.e., proximal stochastic gradient descent (ProxSGD) and proximal stochastic coordinate descent(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)
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)
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)
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)
Graph ranking plays an important role in many applications , such as page ranking on web graph and entity ranking on social networks. In the 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)
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)