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Advanced Hybrid Information Processing. 6th EAI International Conference, ADHIP 2022, Changsha, China, September 29-30, 2022, Proceedings, Part I

Research Article

Social Network Real Estate Advertisement Push Method Based on Big Data Analysis

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-28787-9_46,
        author={Yun Du and Xuanqun Li},
        title={Social Network Real Estate Advertisement Push Method Based on Big Data Analysis},
        proceedings={Advanced Hybrid Information Processing. 6th EAI International Conference, ADHIP 2022, Changsha, China, September 29-30, 2022, Proceedings, Part I},
        proceedings_a={ADHIP},
        year={2023},
        month={3},
        keywords={Big data analysis Social network Real estate advertising Advertising push Text features User features},
        doi={10.1007/978-3-031-28787-9_46}
    }
    
  • Yun Du
    Xuanqun Li
    Year: 2023
    Social Network Real Estate Advertisement Push Method Based on Big Data Analysis
    ADHIP
    Springer
    DOI: 10.1007/978-3-031-28787-9_46
Yun Du1,*, Xuanqun Li2
  • 1: The University of Manchester
  • 2: Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences)
*Contact email: duyun332@163.com

Abstract

In order to solve the problem of large-scale user attribute identification of real estate advertising push, a social network real estate advertising push method based on big data analysis is proposed. First, according to the real estate advertising push strategy of social networks, it is refined and implemented level by level, focusing on specific target customer groups. Given the initial link of the original blog of the advertiser, the text features of the real estate advertising project are extracted by extracting the basic information of all the blogs in a circular manner. Secondly, analyze the social relations of users, mine the characteristics of social network users based on big data analysis, realize the classification and recognition of user attributes, and calculate the similarity between the two using similarity calculation formula. Finally, the calculation results are sorted in reverse order of similarity to generate a real estate advertisement recommendation list for users. The design method is tested on the epinions data set, and the test results show that the design method can improve the accuracy of recommendation and reduce the overall running time.

Keywords
Big data analysis Social network Real estate advertising Advertising push Text features User features
Published
2023-03-22
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-28787-9_46
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