公开(公告)号：US11100558B1

公开(公告)日：2021-08-24

申请号：US15600540

申请日：2017-05-19

Applicant: Amazon Technologies, Inc.

Inventor： Gaurav Chanda ,  Sanjeev Jain

IPC: G06Q30/00 ,  G06Q30/06 ,  G06Q30/02 ,  H04L29/08 ,  G06N20/00

Abstract: A recommendation service that utilizes a machine learning algorithm trained with association vectors is provided. To allow for filtering of unrelated items, a recommendation service generates vectors that represent a quantization of items within a browse node that are considered complementary or a substitute of a selected item. Using an association vector, a machine learning algorithm can be trained to determine whether a particular item recommendation is considered noise, complementary or a substitute. Thereafter, the recommendation service can utilize the trained machine learning algorithm to filter a set of recommendations to remove items considered to be noise or to prioritize items identified as complementary or a substitute.

公开(公告)号：US11080596B1

公开(公告)日：2021-08-03

申请号：US15623291

申请日：2017-06-14

Applicant: Amazon Technologies, Inc.

Inventor： Roshan Harish Makhijani ,  Soo-Min Pantel ,  Sanjeev Jain ,  Gaurav Chanda

IPC: G06N3/08 ,  G06N5/04 ,  G06F7/523 ,  G06N3/04

Abstract: The present disclosure is directed to filtering co-occurrence data. In one embodiment, a machine learning model can be trained. An output of an intermediate structure of the machine learning model (e.g., an output of an internal layer of a neural network) can be used as a representation of an event. Similarities between representations of events can be determined and used to generate, augment, or modify co-occurrence data.

公开(公告)号：US10102559B1

公开(公告)日：2018-10-16

申请号：US14502557

申请日：2014-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Sanjeev Jain ,  Brent Russell Smith ,  Alexandra Juliet Brasch

IPC: G06Q30/00 ,  G06Q30/06 ,  G06Q30/02 ,  G06F17/30 ,  H04L12/807

Abstract: A recommendation system increases the diversity of item recommendations provided to a target user by using item similarity data to reorder a recommendation set of items complementary to a source item. In one embodiment, the complementary items in the recommendation set are ranked based on a relevance score that represents a degree of relevance to the source item. The ranked recommendation set is then reordered based on overlap scores that represent degrees of similarity between particular items so that adjacent items and/or groups of items have less than a threshold degree of similarity. The overlap scores may be generated based on an automated comparison of user item viewing behavior, item attributes, item content, or based on another measure of item similarity.

公开(公告)号：US10984431B1

公开(公告)日：2021-04-20

申请号：US15361799

申请日：2016-11-28

Applicant: Amazon Technologies, Inc.

Inventor： Sanjeev Jain

IPC: G06Q30/02 ,  G06Q30/06 ,  G06F7/02

Abstract: Features are described for improving data transmission by filtering weakly associated items in sequences of item selections. By identifying spurious relationships, data transmissions for weakly related items can be altered or suppressed. The weak relationship may be identified in existing item-to-item associations or for a set of items scheduled for transmission to a user device.

公开(公告)号：US10789637B1

公开(公告)日：2020-09-29

申请号：US16133407

申请日：2018-09-17

Applicant: Amazon Technologies, Inc.

Inventor： Sanjeev Jain ,  Brent Russell Smith ,  Alexandra Juliet Brasch

IPC: G06Q30/00 ,  G06Q30/06 ,  G06Q30/02 ,  H04L12/807 ,  G06F16/2457

Abstract: A server system increases the diversity of item recommendations provided to a target user by using item similarity data to reorder a ranked list of recommended items for presentation to a user. The reordering is performed such that items identified as similar to each other are spaced apart from each other by at least a minimum number of positions. This minimum number may be selected based, e.g., on how many recommended items will initially be presented on a requesting user device, which may depend on the display size or other attributes of the user device. The server system generates a user interface that displays an initial portion of the reordered list. The user interface includes controls for scrolling through the reordered list.