公开(公告)号：US20220295149A1

公开(公告)日：2022-09-15

申请号：US17200691

申请日：2021-03-12

申请人： Adobe Inc.

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

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

摘要： 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.

公开(公告)号：US20220237682A1

公开(公告)日：2022-07-28

申请号：US17159554

申请日：2021-01-27

申请人： ADOBE INC.

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

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

摘要： 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.

公开(公告)号：US20230133522A1

公开(公告)日：2023-05-04

申请号：US17513127

申请日：2021-10-28

申请人： Adobe Inc.

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

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

摘要： 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.

公开(公告)号：US20230116969A1

公开(公告)日：2023-04-20

申请号：US17501191

申请日：2021-10-14

申请人： Adobe Inc.

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

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

摘要： 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.