Recommendation system using improved neural network
AssigneeAmazon Technologies, Inc.
InventorsRejith George Joseph, Oleg Rybakov
Life of this patent
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Abstract
Some aspects of the present disclosure relate to generating and training a neural network by separating historical item interaction data into both inputs and outputs. This may be done, for example, based on date. For example, a neural network machine learning technique may be used to generate a prediction model using a set of inputs that includes both a number of items purchased by a number of users before a certain date as well as some or all attributes of those items, and a set of outputs that includes the items purchased after that date. The items purchased before that date and the associated attributes can be subjected to a time-decay function.
Claims19
A method, comprising:
- by execution of program instructions by a computing system that comprises one or more computing devices:
- maintaining user-specific event histories for each of a plurality of users of an electronic catalog, each event history corresponding to a respective user and comprising event data representing user-item interaction events performed by the user, wherein the event data for each user-item interaction event includes an identifier of a catalog item corresponding to the event and a time of the user-item interaction event;
- dividing each user-specific event history such that user-item interaction events that occurred before a split time are allocated exclusively to an input group and user-item interaction events that occurred after the split time are allocated exclusively to an expected output group;
- and
- training a neural network to predict user-item interaction events falling in the expected output group, wherein training the neural network comprises (1) providing, to input layer nodes of the neural network, identifiers of catalog items represented in the input group, and (2) providing, to output layer nodes of the neural network, identifiers of catalog items represented in the expected output group.
A system, comprising:
- a data repository that stores user-specific event histories for each of a plurality of users of an electronic catalog, each event history corresponding to a respective user and comprising event data representing user-item interaction events performed by the user, wherein the event data for each user-item interaction event includes an identifier of a catalog item corresponding to the event and a time of the user-item interaction event;
- and
- a computing system comprising one or more computing devices, the computing system programmed with executable program instructions to train a neural network by a process that comprises:
- dividing the user-specific event histories such that user-item interaction events that occurred before a split time are allocated exclusively to an input group and user-item interaction events that occurred after the split time are allocated exclusively to an expected output group;
- and
- training the neural network to predict user-item interaction events falling in the expected output group, wherein training the neural network comprises (1) providing, to input layer nodes of the neural network, identifiers of catalog items represented in the input group, and (2) providing, to output layer nodes of the neural network, identifiers of catalog items represented in the expected output group.
Non-transitory computer storage that stores program instructions that direct a computing system to perform a process that comprises:
- maintaining user-specific event histories for each of a plurality of users of an electronic catalog, each event history corresponding to a respective user and comprising event data representing user-item interaction events performed by the user, wherein the event data for each user-item interaction event includes an identifier of a catalog item corresponding to the event and a time of the user-item interaction event;
- dividing the user-specific event histories such that user-item interaction events that occurred before a split time are allocated exclusively to an input group and user-item interaction events that occurred after the split time are allocated exclusively to an expected output group;
- and
- training a neural network to predict user-item interaction events falling in the expected output group, wherein training the neural network comprises (1) providing, to input layer nodes of the neural network, identifiers of catalog items represented in the input group, and (2) providing, to output layer nodes of the neural network, identifiers of catalog items represented in the expected output group.
Dependencies are inferred from the claim text.