公开(公告)号：US10699321B2

公开(公告)日：2020-06-30

申请号：US15785934

申请日：2017-10-17

Applicant: Adobe Inc.

Inventor： Balaji Krishnamurthy ,  Nikaash Puri

IPC: G06Q30/00 ,  G06Q30/06

Abstract: A digital medium environment is described to facilitate recommendations based on vectors generated using feature word embeddings. A recommendation system receives data that describes at least one attribute for a user profile, at least one item, and an interaction between the user profile and the at least one item. The recommendation system associates each user profile attribute, each item, and each interaction between a user profile and an item as a word, using natural language processing, and combines the words into sentences. The sentences are input to a word embedding model to determine feature vector representations describing relationships between the user profile attributes, items, and explicit and implicit interactions. From the feature vector representations, the recommendation system ascertains a similarity between different features. Thus, the recommendation system can provide customized recommendations based on implicit interactions, even for a user profile that is not associated with any historical interaction data.

公开(公告)号：US20200053403A1

公开(公告)日：2020-02-13

申请号：US16057729

申请日：2018-08-07

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nikaash Puri ,  Eshita Shah ,  Balaji Krishnamurthy ,  Nupur Kumari ,  Mayank Singh ,  Akash Rupela

IPC: H04N21/25 ,  G06N99/00 ,  H04N21/475 ,  H04N21/258 ,  H04N21/2668

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.

公开(公告)号：US20200051118A1

公开(公告)日：2020-02-13

申请号：US16057743

申请日：2018-08-07

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nupur Kumari ,  Nikaash Puri ,  Mayank Singh ,  Eshita Shah ,  Balaji Krishnamurthy ,  Akash Rupela

IPC: G06Q30/02 ,  G06N99/00

Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

公开(公告)号：US10558887B2

公开(公告)日：2020-02-11

申请号：US15831160

申请日：2017-12-04

Applicant: Adobe Inc.

Inventor： Shagun Sodhani ,  Nikaash Puri

IPC: G06K9/62 ,  G06F16/583

Abstract: In implementations of digital image search based on arbitrary image features, a server computing device maintains an images database of digital images, and includes an image search system that receives a search input as a digital image depicting image features, and receives search criteria of depicted image features in the digital image. The image search system can then determine similar images to the received digital image based on similarity criterion corresponding to the search criteria. A trained image model of the image search system is applied to determine an image feature representation of the received digital image. A feature mask model of the image search system is applied to the image feature representation to determine a masked feature representation of the received digital image. The masked feature representation of the received digital image is compared to a masked feature representation of each respective database image to identify the similar images.

公开(公告)号：US20190156216A1

公开(公告)日：2019-05-23

申请号：US15815899

申请日：2017-11-17

Applicant: Adobe Inc.

Inventor： Piyush Gupta ,  Nikaash Puri ,  Balaji Krishnamurthy

IPC: G06N3/08 ,  G06N5/02 ,  G06F15/18 ,  G06F17/30

Abstract: A technique is disclosed for generating class level rules that globally explain the behavior of a machine learning model, such as a model that has been used to solve a classification problem. Each class level rule represents a logical conditional statement that, when the statement holds true for one or more instances of a particular class, predicts that the respective instances are members of the particular class. Collectively, these rules represent the pattern followed by the machine learning model. The techniques are model agnostic, and explain model behavior in a relatively easy to understand manner by outputting a set of logical rules that can be readily parsed. Although the techniques can be applied to any number of applications, in some embodiments, the techniques are suitable for interpreting models that perform the task of classification. Other machine learning model applications can equally benefit.

公开(公告)号：US10296546B2

公开(公告)日：2019-05-21

申请号：US14551365

申请日：2014-11-24

Applicant: Adobe Inc.

Inventor： Niyati Chhaya ,  Deepak Pai ,  Dhwanit Agarwal ,  Nikaash Puri ,  Paridhi Jain ,  Ponnurangam Kumaraguru

IPC: G06F16/00 ,  G06F16/9535 ,  H04L29/08 ,  G06F16/435 ,  G06F16/335

Abstract: Techniques are disclosed for identifying the same online user across different communication networks, and further creating a unified profile for that user. The unified profile is an aggregation of publicly available user profile attributes across the different networks. In an embodiment, the techniques are implemented as a computer implemented methodology, including: (1) feature space analysis to identify relevant user features that allows for clusterization of the given target network(s), (2) unsupervised candidate selection to identify one or more candidate user profiles from each target network and that are likely belonging to a target user or so-called queried user, and (3) supervised user identification to identify a likely matching user profile for that target user from each target network. A unified user profile can then be built from data taken from all matched user profiles, and effectively allows a marketer to better understand that user and hence execute more informed targeting.