Microsoft Technology Licensing, LLC

Tao Tao

Dengyong Zhou

Christopher J C Burges

Christian Herwarth Borgs

Jennifer Tour Chayes

Krishna Chaitanya Gade

Seyed Vahab Mirrokni

John E Hopcroft

Amit Prakash

Xiao Li

Yan Ke

Christopher J Burges

Xiaodong Fan

Chris J Burges

Denyong Zhou

A spam detection system is disclosed. The system includes a classifier training component that receives a first set of training pages labeled as normal pages and a second set of training pages labeled as spam pages. The training component trains a web page classifier based on both the first set of training pages and the second set of training pages. A spam detector then receives unlabeled web pages uses the web page classifier to classify the unlabeled web pages as spam pages or normal pages.

Link spam detection using smooth classification function

Techniques for index building are described. Clickcounts of respective training URLs may indicate a number of times that corresponding training URLs were clicked in search engine results. A machine learning algorithm implemented on a computer computes a trained model that is then stored. The clickcounts and respective URLs are passed to the machine learning algorithm to train the model to predict probabilities based on feature vectors of URLs. An index of web pages is built for a set of URLs that identify the web pages. Feature vectors for the URLs are computed. Probabilities of the web pages of the URLs being searched in the future by users may be computed by processing the feature vectors with the trained model. The probabilities may be used to determine which of the URLs to include in the index.

User-driven index selection

A SPAM detection system is provided. The system includes a graph clustering component to analyze web data. A link analysis component can be associated with the graph clustering component to facilitate SPAM detection in accordance with the web data.

Spam score propagation for web spam detection

Neighborhood clustering for web spam detection

A collection of web pages is considered as a directed graph in which the pages themselves are nodes and the hyperlinks between the pages are directed edges in the graph. A trusted entity identifies training examples for spam pages and normal pages. A random walk is conducted through the directed graph that includes the collection of web pages and the stationary probabilities, and transitional probabilities, among the nodes in the directed graph are obtained. A classifier training component estimates a classification function that changes slowly on densely connected subgraphs within the directed graph. The classification function assigns a value to each of the nodes in the directed graph and identifies them as spam or normal pages based upon whether the value meets a given function threshold value.

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Bellevue, WA, United States of America

Tao Tao