公开(公告)号：US11257144B1

公开(公告)日：2022-02-22

申请号：US15861534

申请日：2018-01-03

Applicant: Amazon Technologies, Inc.

Inventor： Andrew Dennis Hamel ,  Lisa Jane Hinegardner ,  Vijai Mohan ,  Srikanth Thirumalai

IPC: G06Q30/00 ,  G06Q30/06 ,  G06F16/9535

Abstract: A network-based enterprise or other system that makes items available for selection to users may implement selecting user interface elements for inclusion with a search result according to item category features of prior item selections. A search request for an item may be received. An item category for the item may be identified and a user interface element type selection model for the item category may be accessed to select of user interface element types for inclusion in a display of a search result in response to the search request. The user interface element type selection model for the item category may be generated based on features of previous item selections in the identified item category. Content for the selected user interface elements may be determined and a display of the search result may be provided that includes user interface elements generated according to the selected type and identified content may be included.

公开(公告)号：US10726334B1

公开(公告)日：2020-07-28

申请号：US15483859

申请日：2017-04-10

Applicant: Amazon Technologies, Inc.

Inventor： Eiman Mohamed Hamdy Elnahrawy ,  Vijai Mohan ,  Eric Nalisnick

IPC: G06N3/08 ,  G06N3/02

Abstract: The present disclosure is directed to generating and using a machine learning model, such as a neural network, by augmenting another machine learning model with an additional parameter. The additional parameter may be connected to some or all nodes of an internal layer of the neural network. A machine learning model can determine a value associated with the additional parameter using non-behavior or non-event-based information. The machine learning model can be trained using non-behavior or non-event-based information and parameter values of the other machine learning model.

公开(公告)号：US09817846B1

公开(公告)日：2017-11-14

申请号：US14639109

申请日：2015-03-04

Applicant: Amazon Technologies, Inc.

Inventor： Sriram Srinivasan ,  Houssam Nassif ,  Vijai Mohan ,  Vishwanathan Swaminathan ,  Mitchell Howard Goodman

IPC: G06F17/00 ,  G06F17/30

CPC classification number: G06F17/3053 ,  G06F17/30867

Abstract: The arrangement and selection of digital content to present to a user can be based upon criteria such as profitability or interest to a user. The selection can also be made to ensure that a diversity of item content is presented. The selection can utilize various rules or policies for diversity at the category level or item feature level, among other such options. In addition to selection diversity, the placement of item content displayed can satisfy various diversity criteria in order to ensure diversity of display as well.

公开(公告)号：US09767409B1

公开(公告)日：2017-09-19

申请号：US14673407

申请日：2015-03-30

Applicant: Amazon Technologies, Inc.

Inventor： Roshan Harish Makhijani ,  Benjamin Thomas Cohen ,  Grant Michael Emery ,  Madhu Madhava Kurup ,  Vijai Mohan

IPC: G06E1/00 ,  G06E3/00 ,  G06F15/18 ,  G06G7/00 ,  G06N3/08 ,  G06F17/30

CPC classification number: G06F17/3082 ,  G06N3/0445

Abstract: Features are disclosed for identifying and routing items for tagging using a latent feature model, such as a recurrent neural network language model (RNNLM). The model may be trained to identify latent features for catalog items such as movies, books, food items, beverages, and the like. Based on similarities in latent features, tags previous assigned to items may be applied to untagged items. Application may be manual or automatic. In either case, resources need to be balances to ensure efficient tagging of items. The included features help to identify and direct these limited tagging resources.

公开(公告)号：US10970629B1

公开(公告)日：2021-04-06

申请号：US15442453

申请日：2017-02-24

Applicant: Amazon Technologies, Inc.

Inventor： Leo Parker Dirac ,  Oleg Rybakov ,  Vijai Mohan

IPC: G06N3/08 ,  G06N3/04

Abstract: The present disclosure is directed to reducing model size of a machine learning model with encoding. The input to a machine learning model may be encoded using a probabilistic data structure with a plurality of mapping functions into a lower dimensional space. Encoding the input to the machine learning model results in a compact machine learning model with a reduced model size. The compact machine learning model can output an encoded representation of a higher-dimensional space. Use of such a machine learning model can include decoding the output of the machine learning model into the higher dimensional space of the non-encoded input.

公开(公告)号：US09934332B1

公开(公告)日：2018-04-03

申请号：US14742930

申请日：2015-06-18

Applicant: Amazon Technologies, Inc.

Inventor： Srikanth Thirumalai ,  Vijai Mohan

IPC: G06F17/30 ,  G06Q30/06

CPC classification number: G06F17/30973 ,  G06F17/30412 ,  G06F17/30719 ,  G06F17/30867 ,  G06Q30/0631

Abstract: Disclosed are various embodiments for a similarity service. Multiple samplings of user accounts are randomly selected from a pool of user accounts. Interaction history data for each of the user accounts is used to determine item similarities corresponding to each of the user account samplings. The item similarity data is aggregated to determine similar items.

公开(公告)号：US09881226B1

公开(公告)日：2018-01-30

申请号：US14864605

申请日：2015-09-24

Applicant: Amazon Technologies, Inc.

Inventor： Oleg Rybakov ,  Matias Omar Gregorio Benitez ,  Leo Parker Dirac ,  Rejith George Joseph ,  Vijai Mohan ,  Srikanth Thirumalai

IPC: G06K9/46 ,  G06T7/00 ,  G06T1/00 ,  G06K9/00

CPC classification number: G06K9/46 ,  G06K9/00201 ,  G06T1/0007 ,  G06T7/0081 ,  G06T2207/10004

Abstract: Recommendations can be generated even in situations where sufficient user information is unavailable for providing personalized recommendations. Instead of generating recommendations for an item based on item type or category, a relation graph can be consulted that enables other items to be recommended that are related to the item in some way, which may be independent of the type or category of item. For example, images of models, celebrities, or everyday people wearing items of clothing, jewelry, handbags, shoes, and other such items can be received and analyzed to recognize those items and cause them to be linked in the relation graph. When generating recommendations or selecting advertisements, the relation graph can be consulted to recommend products that other people have obtained with the item from any of a number of sources, such that the recommendations may be more valuable to the user.

公开(公告)号：US09864951B1

公开(公告)日：2018-01-09

申请号：US14673384

申请日：2015-03-30

Applicant: Amazon Technologies, Inc.

Inventor： Roshan Harish Makhijani ,  Benjamin Thomas Cohen ,  Grant Michael Emery ,  Vijai Mohan

IPC: G06E1/00 ,  G06E3/00 ,  G06F15/18 ,  G06G7/00 ,  G06N3/08

CPC classification number: G06N3/0445 ,  G06F17/276 ,  G06Q30/00

Abstract: Features are disclosed for identifying randomized latent feature language modeling, such as a recurrent neural network language modeling (RNNLM). Sequences of item identifiers may be provided as the language for training the language model where the item identifiers are the words of the language. To avoid localization bias, the sequences may be randomized prior to or during the training process to provide more accurate prediction models.