Yahoo! Inc.

International Business Machines Corporation

Dmitry Pavlov

Byron Edward Dom

Ramnath Balasubramanyan

Jianchang Mao

Pavel Berkhin

Ye Chen

John Canny

Bipin Suresh

Abhishek Goyal

Hui Han

A method and a system are provided for large-scale behavioral targeting for advertising over a network, such as the Internet. In one example, the system receives training data that is processed raw data of user behavior. The system generates selected features by performing feature selection on the training data. The system generates feature vectors from the selected features. The system initializes weights of a behavioral targeting model by scanning the feature vectors once. The system then updates the weights of the behavioral targeting model by scanning iteratively the feature vectors using a multiplicative recurrence.

Large-scale behavioral targeting for advertising over a network

Techniques are described for assigning, to target categories of a target scheme, items that have been obtained from a plurality of sources. In situations in which one or more of the sources has organized its information according to a source scheme that differs from the target scheme, the assignment may be based, in part, on an estimate of the probability that items from a particular source category should be assigned to a particular target category. Such probability estimates may be based on how many training set items associated with the particular source category have been assigned to the particular target category. Source categories may be grouped into clusters. The probability estimates may also be based on how many training set items within the cluster to which the particular source category has been mapped, have been assigned the particular target category.

Assigning into one set of categories information that has been assigned to other sets of categories

Techniques are provided for automatic product categorization. In one aspect, the categorization is based on text and one or more other values associated with a product offering. In another aspect, a first categorization of a product offering is performed and, if the product category chosen is in a set of co-refinable product categories, then a second (or third, fourth and so on) categorization is performed among the set of co-refinable product categories. In a third aspect, products are categorized based on cost. In another aspect, after products are categorized, the products are flagged for further categorization processing if the cost for categorizing the product is beyond a predefined threshold.

Automatic product categorization

A system and method for training an SVM in a scalable manner includes boosting the SVM during training. Specifically, individual SVMs in an ensemble of SVMs are trained using small subsets of a training data set, with data that earlier classifiers in the ensemble incorrectly classified being overrepresented in succeeding subsets. In this way, the speed with which the overall SVM is trained is increased and the memory requirements therefor are reduced, even for relatively large training data sets.

System and method for boosting support vector machines

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San Jose, CA, United States of America

Dmitry Pavlov