Patents by Inventor Renjun HU

Renjun HU 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: 11313694
    Abstract: The present disclosure provides a method and an apparatus for recommending a travel way. The method includes: obtaining historical travel data; determining a plurality of correlations between respective travel ways according to the historical travel data; generating a vector corresponding to each travel way according to the plurality of correlations between respective travel ways; performing learning on an initial vector corresponding to each user and an initial vector corresponding to each starting-and-arrival pair according to the vector corresponding to each travel way and the historical travel data, to obtain a vector corresponding to each user and a vector corresponding to each starting-and-arrival pair; and recommending a travel way according to the vector corresponding to each travel way, the vector corresponding to each user and the vector corresponding to each starting-and-arrival pair.
    Type: Grant
    Filed: January 15, 2020
    Date of Patent: April 26, 2022
    Assignee: BAIDU ONLINE NETWORK TECHNOLOGY (BEIJING) CO., LTD.
    Inventors: Renjun Hu, Hao Liu, Hui Xiong, Ting Li, Yanjie Fu
  • Publication number: 20210254992
    Abstract: The present disclosure provides a method for optimizing a tag of a point of interest s(POI). The method includes: obtaining first portrait feature data of each POI in a plurality of POIs and second portrait feature data of each tag in a plurality of marked tags corresponding to the plurality of POIs; mapping the first portrait feature data of each POI and the second portrait feature data of each tag to a metric space to obtain a first feature vector of each POI and a second feature vector of each tag; and optimizing at least one marked tag corresponding to a target POI based on a vector similarity between a first feature vector of the target POI and a second feature vector of at least one tag. The present disclosure provides an apparatus for optimizing a tag of a POI, an electronic device and a computer readable medium.
    Type: Application
    Filed: September 29, 2020
    Publication date: August 19, 2021
    Inventors: Jingbo ZHOU, Renjun HU, Airong JIANG, Jianguo DUAN, Hui XIONG
  • Publication number: 20210192378
    Abstract: The present application proposes a quantitative analysis method for a user decision-making behavior and an apparatus, which relate to the fields of big data calculation and artificial intelligence in computer technology. At least one quantified decision factor related to making a target decision by a user is inputted into a machine learning model; the machine learning model further analyzes the decision factor; and finally a prediction result of making the target decision by the user is determined according to an output of the machine learning model. Therefore, it is possible to analyze the decision factor for making the target decision by the user to obtain the prediction result of making the target decision, thus enriching analysis needs for the user decision-making behavior.
    Type: Application
    Filed: December 21, 2020
    Publication date: June 24, 2021
    Inventors: Xinjiang LU, Renjun HU, Hui XIONG
  • Publication number: 20210019564
    Abstract: Embodiments of the present disclosure provide a method and apparatus for optimizing a tag of a point of interest. The method includes: acquiring a set of points of interest and a set of tags of points of interest in the set of points of interest; generating a point of interest-tag matrix based on the set of points of interest and the set of tags of points of interest in the set of points of interest; extracting a feature of a point of interest-tag in the point of interest-tag matrix; inputting the feature of the point of interest-tag in the point of interest-tag matrix into a pre-trained ternary self-adaptive collaborative learning model, to obtain a point of interest-tag score matrix; and optimizing the set of tags of points of interest in the set of points of interest based on the point of interest-tag score matrix.
    Type: Application
    Filed: April 24, 2020
    Publication date: January 21, 2021
    Inventors: Jingbo Zhou, Renjun Hu, Hui Xiong
  • Publication number: 20200225055
    Abstract: The present disclosure provides a method and an apparatus for recommending a travel way. The method includes: obtaining historical travel data; determining a plurality of correlations between respective travel ways according to the historical travel data; generating a vector corresponding to each travel way according to the plurality of correlations between respective travel ways; performing learning on an initial vector corresponding to each user and an initial vector corresponding to each starting-and-arrival pair according to the vector corresponding to each travel way and the historical travel data, to obtain a vector corresponding to each user and a vector corresponding to each starting-and-arrival pair; and recommending a travel way according to the vector corresponding to each travel way, the vector corresponding to each user and the vector corresponding to each starting-and-arrival pair.
    Type: Application
    Filed: January 15, 2020
    Publication date: July 16, 2020
    Inventors: Renjun HU, Hao LIU, Hui XIONG, Ting LI, Yanjie FU
  • Publication number: 20200042902
    Abstract: The present disclosure provides a method for building a user visit inference model, an apparatus and a storage medium. According to positioning data of each user in a plurality of users, a set of staying points of the each user is determined, wherein the set of staying points is a cluster set of staying points; according to the sets of staying points of all users, a group relationship of the users is formed; according to the set of staying points of the each user, a visit relationship of the each user is formed; and vector characterization learning is performed on the group relationship and the visit relationships that are formed to obtain the user visit inference model. In the user visit inference model obtained by the above building method, the user group relationship and the visit relationship, and has higher prediction accuracy compared with the solution of the prior art.
    Type: Application
    Filed: October 16, 2019
    Publication date: February 6, 2020
    Applicant: Baidu Online Network Technology (Beijing) Co., Ltd.
    Inventors: Yanyan LI, Renjun HU, Jianguo DUAN, Airong JIANG, Hui XIONG