Supplementing a trained model using incremental data in making item recommendations

摘要

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.

主权项

1. A method comprising:
receiving, using at least one processor, an item recommendation request, the request identifying a user; responsive to a determination made using web page interaction data that the user is a frequent user, using item scoring in a model trained using the web page interaction data for a plurality of users, the item scoring identifying a plurality of scored items and the corresponding scores; responsive to a determination made using the web page interaction data that the user is an infrequent user, the at least one processor;
identifying, using a current item identified from behavior of the user and the trained model, a probability for each cluster identified in the trained model that the user belongs to the cluster; andgenerating the plurality of scored items, each item of the plurality of scored items having an item score determined using the item's cluster score and the probability that the user belongs to the cluster; selecting, by the at least one processor, items from the plurality of scored items based on the item scoring; and providing, by the at least one processor, the selected items as item recommendations in response to the request.