User Network Location Identification Based on A Big Data Similarity Model
CAI Jinsong
Abstract：In order to address the issues of low recall rates and long response times in the current location positioning system for network users,this paper proposes a location recognition method based on a big data similarity model.Network data from multiple sources,such as base station signals and user trajectories,is collected and standardized.Multi-dimensional features,such as spatio-temporal,behavioral,and network features,are extracted.Different weight coefficients are assigned to these features based on an attention mechanism in order to construct a big data similarity model.The similarity of the spatio-temporal,behavioral and network features is then calculated using the Frechet,EMD and cosine similarity measurement methods.Comprehensive similarity is then obtained through weighted summation,combining attention weight coefficients to precisely locate and identify the user's network position.Experimental results demonstrate that applying the proposed method achieves an optimal user network location recognition accuracy of 99％with a minimum response time of 0.08 seconds.
Keywords：user IP locationnetwork behavior data collection and processinglocation identificationbig data similarity modelpositioning error
Publication Date：2025-12-30
Online Publishing Date：2026-01-17(First online date of this platform, not the publication date of the document)
Pages：7( 64-70 )