Patents by Inventor Mengru WANG

Mengru WANG has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 12001518
    Abstract: A method for predicting matching degree between a resume and a post, and a related device are provided in this disclosure. In the method for predicting the matching degree between the resume and the post, and the related device according to this disclosure, firstly the semi-structured keys and values in post information and resume information and their source are obtained. Then, the matching degree between the resume information and the post information is predicted by a prediction model including a cascaded pre-trained language model, a Transformer encoder and a single label classification model, based on the keys and values of a respective post information and resume information attribute, and corresponding source representations. Thus, by comprehensively searching internal interaction and external interaction of semi-structured multivariate attributes in person-post matching, the matching result is more accurate.
    Type: Grant
    Filed: December 16, 2022
    Date of Patent: June 4, 2024
    Assignee: National University of Defense Technology
    Inventors: Honghui Chen, Taihua Shao, Chengyu Song, Miao Jiang, Mengru Wang, Xin Zhang, Fei Cai, Dengfeng Liu, Siyuan Wang
  • Publication number: 20230195850
    Abstract: A method for predicting matching degree between a resume and a post, and a related device are provided in this disclosure. In the method for predicting the matching degree between the resume and the post, and the related device according to this disclosure, firstly the semi-structured keys and values in post information and resume information and their source are obtained. Then, the matching degree between the resume information and the post information is predicted by a prediction model including a cascaded pre-trained language model, a Transformer encoder and a single label classification model, based on the keys and values of a respective post information and resume information attribute, and corresponding source representations. Thus, by comprehensively searching internal interaction and external interaction of semi-structured multivariate attributes in person-post matching, the matching result is more accurate.
    Type: Application
    Filed: December 16, 2022
    Publication date: June 22, 2023
    Inventors: Honghui CHEN, Taihua SHAO, Chengyu SONG, Miao JIANG, Mengru WANG, Xin ZHANG, Fei CAI, Dengfeng LIU, Siyuan WANG
  • Patent number: 11551284
    Abstract: A session-based recommendation method and device according to one or more embodiments of this disclosure are provided, which use a pre-trained recommendation model to perform item recommend. The method includes following contents: a directed session graph is constructed according to a session to be predicted; the directed session graph is then input into a gated graph neural network which outputs the item embedding vector; a user's dynamic preference is determined according to a user's current preference and a first long-term preference, the current preference is an item embedding vector of a last item in the session and the first long-term preference is determined according to the item embedding vector and an importance score of the item; a prediction score of a respective item is determined according to the dynamic preference and the item embedding vector; and a recommended item is output according to the prediction score of the respective item.
    Type: Grant
    Filed: May 16, 2022
    Date of Patent: January 10, 2023
    Assignee: National University of Defense Technology
    Inventors: Fei Cai, Chengyu Song, Yitong Wang, Zhiqiang Pan, Xin Zhang, Mengru Wang, Wanyu Chen, Honghui Chen
  • Publication number: 20220374962
    Abstract: A session-based recommendation method and device according to one or more embodiments of this disclosure are provided, which use a pre-trained recommendation model to perform item recommend. The method includes following contents: a directed session graph is constructed according to a session to be predicted; the directed session graph is then input into a gated graph neural network which outputs the item embedding vector; a user's dynamic preference is determined according to a user's current preference and a first long-term preference, the current preference is an item embedding vector of a last item in the session and the first long-term preference is determined according to the item embedding vector and an importance score of the item; a prediction score of a respective item is determined according to the dynamic preference and the item embedding vector; and a recommended item is output according to the prediction score of the respective item.
    Type: Application
    Filed: May 16, 2022
    Publication date: November 24, 2022
    Inventors: Fei CAI, Chengyu SONG, Yitong WANG, Zhiqiang PAN, Xin ZHANG, Mengru WANG, Wanyu CHEN, Honghui CHEN