Yahoo! Inc.

Wei Du

Jessi Dong

Biswadeep Nag

Qiong Zhang

Wei Yu

Wei Chu

Seung-Taek Park

Michael Helman

Uminder Singh

Charles Wang

Incremental training data is used to supplement a trained model to provide personalized recommendations for a user. The personalized recommendations can be made by taking into account the user's behavior, such as, without limitation, the user's short and long term web page interactions, to identify item recommendations. A trained model is generated from training data indicative of the web page interaction data collected from a plurality of users. Incremental training data indicative of other web page interaction data can be used to supplement the trained model, or in place of the trained model. Incremental training data can be indicative of user behavior collected more recently than the data used to train the model, for example.

Supplementing a trained model using incremental data in making item recommendations

A system and method are disclosed for optimizing the performance of an advertisement. The advertisement may be targeted based on correlations between advertisements and/or users. The correlations may be used to improve the click-through rate of advertisements. As data is collected and feedback is received, the correlation between ads and users may be updated, so that an advertiser's campaign can optimize its targeting of users.

System for optimizing ad performance at campaign running time

Two items are determined to be similar to each not only based on previous actual user behavior, but also based on the observed relatedness of the characteristics of those two items. A first characteristic and a second characteristic are determined to have some affinity for each other if a high proportion of users who select items having the first characteristics also select items that have the second characteristic, and vice-versa. Two items having characteristics with high affinity for each other are determined to have some similarity to each other, even if very few or no users who selected one of those items ever selected the other of those items. A first item that is determined to be sufficiently similar to second item in this manner may be recommended to a user who has selected the second item as potentially also being of interest to that user.

Predicting item-item affinities based on item features by regression

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Burbank, CA, United States of America

Wei Du