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    Multimodal Sequential Recommendation with Window Co-Attention
    1.
    发明申请
    Multimodal Sequential Recommendation with Window Co-Attention 有权

    公开(公告)号：US20220295149A1

    公开(公告)日：2022-09-15

    申请号：US17200691

    申请日：2021-03-12

    Applicant: Adobe Inc.

    Inventor： Handong Zhao ,  Zhankui He ,  Zhe Lin ,  Zhaowen Wang ,  Ajinkya Gorakhnath Kale

    IPC: H04N21/466 ,  H04N21/4722 ,  H04N21/45 ,  G06N3/08

    Abstract: A multimodal recommendation identification system analyzes data describing a sequence of past content item interactions to generate a recommendation for a content item for a user. An indication of the recommended content item is provided to a website hosting system or recommendation system so that the recommended content item is displayed or otherwise presented to the user. The multimodal recommendation identification system identifies a content item to recommend to the user by generating an encoding that encodes identifiers of the sequence of content items the user has interacted with and generating encodings that encode multimodal information for content items in the sequence of content items the user has interacted with. An aggregated information encoding for a user based on these encodings and a system analyzes the content item sequence encoding and interaction between the content item sequence encoding and the multiple modality encodings to generate the aggregated information encoding.

    SCALABLE ARCHITECTURE FOR RECOMMENDATION
    2.
    发明申请
    SCALABLE ARCHITECTURE FOR RECOMMENDATION 有权

    公开(公告)号：US20220237682A1

    公开(公告)日：2022-07-28

    申请号：US17159554

    申请日：2021-01-27

    Applicant: ADOBE INC.

    Inventor： Handong Zhao ,  Zhankui He ,  Zhaowen Wang ,  Zhe Lin ,  Ajinkya Kale ,  Fengbin Chen

    IPC: G06Q30/06 ,  G06N3/04 ,  G06N3/08 ,  G06K9/62

    Abstract: Systems and methods for item recommendation are described. Embodiments identify a sequence of items selected by a user, embed each item of the sequence of items to produce item embeddings having a reduced number of dimensions, predict a next item based on the item embeddings using a recommendation network, wherein the recommendation network includes a sequential encoder trained based at least in part on a sampled softmax classifier, and wherein predicting the next item represents a prediction that the user will interact with the next item, and provide a recommendation to the user, wherein the recommendation includes the next item.

    Digital content query-aware sequential search
    3.
    发明授权
    Digital content query-aware sequential search 有权

    公开(公告)号：US12124439B2

    公开(公告)日：2024-10-22

    申请号：US17513127

    申请日：2021-10-28

    Applicant: Adobe Inc.

    Inventor： Handong Zhao ,  Zhe Lin ,  Zhaowen Wang ,  Zhankui He ,  Ajinkya Gorakhnath Kale

    IPC: G06F16/245 ,  G06F16/248 ,  G06N20/00

    CPC classification number: G06F16/245 ,  G06F16/248 ,  G06N20/00

    Abstract: Digital content search techniques are described that overcome the challenges found in conventional sequence-based techniques through use of a query-aware sequential search. In one example, a search query is received and sequence input data is obtained based on the search query. The sequence input data describes a sequence of digital content and respective search queries. Embedding data is generated based on the sequence input data using an embedding module of a machine-learning model. The embedding module includes a query-aware embedding layer that generates embeddings of the sequence of digital content and respective search queries. A search result is generated referencing at least one item of digital content by processing the embedding data using at least one layer of the machine-learning model.

    Locally constrained self-attentive sequential recommendation
    4.
    发明授权
    Locally constrained self-attentive sequential recommendation 有权

    公开(公告)号：US12019671B2

    公开(公告)日：2024-06-25

    申请号：US17501191

    申请日：2021-10-14

    Applicant: Adobe Inc.

    Inventor： Handong Zhao ,  Zhankui He ,  Zhaowen Wang ,  Ajinkya Gorakhnath Kale ,  Zhe Lin

    IPC: G06F16/43 ,  G06F16/438 ,  G06F16/44 ,  G06N3/045

    CPC classification number: G06F16/438 ,  G06F16/447 ,  G06N3/045

    Abstract: Digital content search techniques are described. In one example, the techniques are incorporated as part of a multi-head self-attention module of a transformer using machine learning. A localized self-attention module, for instance, is incorporated as part of the multi-head self-attention module that applies local constraints to the sequence. This is performable in a variety of ways. In a first instance, a model-based local encoder is used, examples of which include a fixed-depth recurrent neural network (RNN) and a convolutional network. In a second instance, a masking-based local encoder is used, examples of which include use of a fixed window, Gaussian initialization, and an adaptive predictor.

    PREDICTIVE AGENTS FOR MULTI-ROUND CONVERSATIONAL RECOMMENDATIONS OF BUNDLED ITEMS
    5.
    发明公开
    PREDICTIVE AGENTS FOR MULTI-ROUND CONVERSATIONAL RECOMMENDATIONS OF BUNDLED ITEMS 审中-公开

    公开(公告)号：US20240169410A1

    公开(公告)日：2024-05-23

    申请号：US17980790

    申请日：2022-11-04

    Applicant: Adobe Inc.

    Inventor： Handong Zhao ,  Zhankui He ,  Tong Yu ,  Fan Du ,  Sungchul Kim

    IPC: G06Q30/06

    CPC classification number: G06Q30/0631

    Abstract: Techniques for predicting and recommending item bundles in a multi-round conversation to discover a target item bundle that would be accepted by a client. An example method includes receiving an input response in reply to a first item bundle that includes one or more items. A state model is updated to reflect the input response to the first item bundle. A machine-learning (ML) conversation module is applied to the state model to determine an action type as a follow-up to the input response to the first item bundle. Based on selection of a recommendation action as the action type, an ML bundling module is applied to the state model to generate a second item bundle different than the first item bundle. The second item bundle is then recommended.

    Digital Content Query-Aware Sequential Search
    7.
    发明申请
    Digital Content Query-Aware Sequential Search 有权

    公开(公告)号：US20230133522A1

    公开(公告)日：2023-05-04

    申请号：US17513127

    申请日：2021-10-28

    Applicant: Adobe Inc.

    Inventor： Handong Zhao ,  Zhe Lin ,  Zhaowen Wang ,  Zhankui He ,  Ajinkya Gorakhnath Kale

    IPC: G06F16/245 ,  G06F16/248 ,  G06N20/00

    Abstract: Digital content search techniques are described that overcome the challenges found in conventional sequence-based techniques through use of a query-aware sequential search. In one example, a search query is received and sequence input data is obtained based on the search query. The sequence input data describes a sequence of digital content and respective search queries. Embedding data is generated based on the sequence input data using an embedding module of a machine-learning model. The embedding module includes a query-aware embedding layer that generates embeddings of the sequence of digital content and respective search queries. A search result is generated referencing at least one item of digital content by processing the embedding data using at least one layer of the machine-learning model.

    Locally Constrained Self-Attentive Sequential Recommendation
    8.
    发明申请
    Locally Constrained Self-Attentive Sequential Recommendation 有权

    公开(公告)号：US20230116969A1

    公开(公告)日：2023-04-20

    申请号：US17501191

    申请日：2021-10-14

    Applicant: Adobe Inc.

    Inventor： Handong Zhao ,  Zhankui He ,  Zhaowen Wang ,  Ajinkya Gorakhnath Kale ,  Zhe Lin

    IPC: G06F16/438 ,  G06F16/44 ,  G06N3/04

    Abstract: Digital content search techniques are described. In one example, the techniques are incorporated as part of a multi-head self-attention module of a transformer using machine learning. A localized self-attention module, for instance, is incorporated as part of the multi-head self-attention module that applies local constraints to the sequence. This is performable in a variety of ways. In a first instance, a model-based local encoder is used, examples of which include a fixed-depth recurrent neural network (RNN) and a convolutional network. In a second instance, a masking-based local encoder is used, examples of which include use of a fixed window, Gaussian initialization, and an adaptive predictor.

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