Supplementing a trained model using incremental data in making item recommendations

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...

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Bibliographic Details
Main Authors NAG BISWADEEP, YU WEI, DONG JESSI, ZHANG QIONG, DU WEI
Format Patent
LanguageEnglish
Published 24.06.2014
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary: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.
Bibliography:Application Number: US201213618647