Walmart Apollo, LLC

Fujitsu Corporation

International Business Machines Corporation

Beijing Sigone Venture Limited

Gmedia Technology (beijing) Co., Ltd.

Wal-mart Stores, Inc.

Other

Gmedia Corporation

Beijing Bigcode Technology Co., Ltd.

Boe Technology Group Co., Ltd.

Beijing Boe Optoelectronics Technology Co., Ltd.

Baidu Online Network Technology (beijing) Co., Ltd.

Chongqing Boe Optoelectronics Technology Co., Ltd.

Shanghai Xiaodu Technology Co. Ltd.

Tsinghua University

Wei Shen

Lu Wang

Rujie Liu

Ioannis Pavlidis

Vahid Jalalibarsari

Zhao Zhao

Xiao Ping Guo

Jia Bing Liu

Li Duo Zhong

Mindi Yuan

Yannis Pavlidis

Xuan Cao

Guo Tong Xie

Wen Sun

Gang Hu

Jing Mei

Zoheb Vacheri

Chenxi Liu

Ronald Benson

Zigeng Wang

Jun Li

Xia Zhao

Siping Wang

Kaijun Cao

Hongqiang Zhang

Chris Undernehr

Cheng Jie

Yu Chen Zhou

Lin Xu

Lei Shi

Laura Wynter

Ran Xu

Anand Rajaraman

Xu Wu

Yi Qin Yu

Yue Pan

Jun Yao

Zhonghao Huang

Xing Zhi Sun

Yongliang Zhao

Haoran Gao

Pengfei Wang

Mengmeng Zhang

Li Wan

Qiang Ju

Jian Xie

Mukesh Jain

Chao Yin

Tao Zhu

Xiaonan Dong

Changzheng Liu

Sayan Maity

Jingya Wang

Georgios Rovatsos

Jingsi Zhang

Zhongji Fan

Yuan Xie

Jinghai Xu

Shufang Yu

Jie Shao

Xiao Fei Teng

Jiabing Liu

Xiaowei Guo

Liduo Zhong

Si Ping Wang

Feng Ai

Jing Lu

Qiuwen Wang

Yaonan Liu

Xiao Wei Guo

Glass rinser

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform functions comprising: calculating a first user propensity score and a second user propensity score for a user based on at least one feature vector of historical data of the user; using the first user propensity score to place the user into a first segment; using the second user propensity score to place the user into a second segment different than the first segment; and facilitating delivery of a message to an electronic device of the user based on the first segment and the second segment. Other embodiments are disclosed.

Systems and methods for behavior based messaging

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform determining a respective interaction metric between at least two respective keywords of a plurality of keywords; constructing, using each respective interaction metric, as determined, a graph comprising a plurality of nodes connected by at least one edge; identifying one or more clusters of nodes using the graph; optimizing each cluster of nodes of the one or more clusters of nodes; and facilitating altering a graphical user interface (GUI) of an electronic device based upon a cluster of nodes of the one or more clusters of nodes, as identified and optimized. Other embodiments are disclosed herein.

Systems and methods for keyword categorization

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of storing, for each respective customer of one or more customers, respective customer information in a customer database; predicting, using a machine learning lifetime value (LTV) update model, a respective LTV for each respective customer of the one or more customers; determining an online advertisement for each respective customer of the one or more customers using the respective LTV, as predicted, for each respective customer of the one or more customers; and coordinating displaying the online advertisement for at least a portion of the one or more customers. Other embodiments are disclosed herein.

Systems and methods for determining customer lifetime value

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to perform (1) receiving a request to generate one or more campaigns; (2) determining one or more predicted bids for one or more keywords in the one or more campaigns; (3) adjusting the one or more predicted bids for the one or more campaigns; (4) pacing the one or more predicted bid, as adjusted, for the one or more campaigns; and repeating (2)-(4) at one or more periodic intervals. Other embodiments are disclosed herein.

Systems and methods for improved online predictions

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: determining one or more similarity scores between at least two of: a first item in a hierarchical categorization containing items; a category in the hierarchical categorization; or a topic; filtering out at least one potential match from one or more potential matches using the one or more similarity scores; ranking the one or more potential matches, as filtered, using the one or more similarity scores; re-ranking the one or more potential matches, as filtered, using additional data about at least one of: the first item in the hierarchical categorization containing the items; the category in the hierarchical categorization; or the topic; and inserting one or more hyperlinks into an anchor webpage, the one or more hyperlinks configured to navigate a user from an anchor webpage to one or more potential match webpages associated with the one or more potential matches, as filtered and re-ranked. Other embodiments are disclosed herein.

Systems and methods for inserting links

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform extracting meta features for an item to generate sparse feature embeddings for the item; reducing, using a multilayer perceptron, a dimension of the sparse feature embeddings to generate a representation vector for the meta features; extracting, using a recurrent neural network, sequential data from dense traffic features for the item over a period of time; and inputting the representation vector for the meta features and the sequential data from the dense traffic features into a multilayer neural network with a rectified linear unit (ReLU) activation function and a scoring layer to generate one or more performance metrics for the item. Other embodiments are disclosed.

Deep learning-based revenue-per-click prediction model framework

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to perform functions comprising: tracking interaction data for one or more webpages of a website; determining a content score for the one or more webpages of the website; determining a link equity score for the one or more webpages of the website; classifying the one or more webpages of the website into one or more classifications using the interaction data, the content score, and the link equity score; and removing the one or more webpages from a sitemap of the website based on the one or more classifications. Other embodiments are disclosed herein.

Systems and methods for maintaining a sitemap

A method including determining a preferred communication channel. The method can include retrieving, from a user database, subscription data for a user. The method further can include determining a preferred communication channel by: (a) determining a respective normalized propensity score for each of subscribed communication channels; and (b) determining the preferred communication channel from among the subscribed communication channels based on the respective normalized propensity score. The method also can include transmitting, via the preferred communication channel, a message to a user device of the user. Other embodiments are disclosed.

Systems and methods for automatically determining a preferred communication channel for messaging

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving one or more objectives and one or more constraints from a user electronic device of a user; generating a combined objective using the one or more objectives; calculating, using the combined objective, a return per click for an advertisement campaign; determining one or more bids based on (a) the return per click for the advertisement campaign and (b) a return on advertising spend for the advertisement campaign; estimating, using a first predictive algorithm and the one or more bids, an average cost per click; estimating an expected number of clicks for the advertisement campaign based on the average cost per click; determining a total amount used of the one or more constraints for the one or more bids based on the expected number of clicks for the advertisement campaign; and when the total amount used of the one or more constraints for the one or more bids exceeds at least one of the one or more constraints, lowering the one or more bids. Other embodiments are disclosed herein.

Systems and methods for training of multi-objective machine learning algorithms

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of collecting historical data of a user; converting the historical data of the user into at least one feature vector; calculating a first user propensity score for the user using the at least one feature vector; calculating a second user propensity score for the user using the at least one feature vector, the second user propensity score representing a different user propensity than the first user propensity score; normalizing the first user propensity score; normalizing the second user propensity score; using the first user propensity score, as normalized, to place the user into a first segment; using the second user propensity score, as normalized, to place the user into a second segment different than the first segment; and facilitating delivery of a message to the user based on the first segment and the second segment.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving a set of keywords from a graphical user interface of an electronic device of a user; pre-processing at least one keyword of the set of keywords; receiving a hierarchical categorization; pre-processing at least one category of the hierarchical categorization; determining a respective similarity between each of the at least one keyword of the set of keywords and each of the at least one category of the hierarchical categorization; determining a respective confidence level of a most likely category in the hierarchical categorization for each of the at least one keyword of the set of keywords using the respective similarity between each of the at least one keyword of the set of keywords and each of the at least one category of the hierarchical categorization; ranking each of the at least one keyword of the set of keywords using a respective importance of each keyword of the set of keywords and the respective confidence level of the most likely category in the hierarchical categorization; and altering a respective arrangement of at least one of the at least one keyword on the graphical user interface on the electronic device of the user based on the ranking. Other embodiments are disclosed herein.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform receiving a request for a machine learned prediction for an item; determining that a number of entries in item user interaction data of the item is below a predetermined number-of-entries threshold; incorporating category interaction data of a hierarchical category of the item from a predetermined time period into the item user interaction data of the item; determining an initial machine learned prediction for the item using at least the category interaction data, as incorporated into the item user interaction data; and facilitating altering a graphical user interface (GUI) of an electronic device using the initial machine learned prediction for the item. Other embodiments are disclosed herein.

Systems and methods for bootstrapped machine learning algorithm training

An information processing apparatus includes a processor to input each sample image into feature extracting components to obtain at least two features of the sample image, and to cause a classifying component to calculate a classification loss of the sample image based on the at least two features; extract, from each pair of features, a plurality of sample pairs for calculating mutual information between each pair of features; input the plurality of sample pairs into a machine learning architecture corresponding to each pair of features, to calculate an information loss between each pair of features. The processor is to adjust parameters of the feature extracting components, the classifying component and the machine learning architecture by minimizing a sum of classification losses and information losses of sample images in the training set based upon the obtained at least two features of the sample image to calculate the classification losses and the information losses, to obtain the at least two feature extracting components and the classifying component having been trained.

Information processing apparatus and information processing method

Disclosed is a display device, a manufacturing method thereof and a display apparatus. The display device includes a carrier having a first surface and a second surface opposite to each other, and at least one nanotube in the carrier. Each nanotube includes a tube wall and a receiving cavity surrounded by the tube wall. The receiving cavity includes a first open end at the first surface and a second open end at the second surface. The display device further includes a first electrode at the first open end, a second electrode at the second open end and a light-emitting layer between the first electrode and the second electrode.

Display device including OLED surrounded by nanotube extending through carrier, and manufacturing method thereof

Growing community of inventors

Beijing, China

Wei Shen