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Chinese trending search terms popularity rank prediction

journal contribution
posted on 2023-05-18, 17:14 authored by Han, SC, Liang, Y, Hyunsuk Chung, Kim, H, Byeong KangByeong Kang
Baidu, the most popular Chinese search engine, monitors what their users are currently searching and provides top 50 search terms, called trending search terms, in descending order of popularity ranking. The paper focused on predicting the popularity ranking trends of this top trending search terms in Baidu. Based on the data analysis, two issues were identified that could affect accuracy of using the ranking data for predicting the popularity of trending searched terms. Firstly, all trending terms are disappeared from the top 50 terms list when the popularity is getting lower. However, there are several trending terms that reappear to the top 50 terms list after they disappeared. New distinct search terms can be differentiated from reappearances of old terms so we proposed the term distinction model by using the related news articles of a trending search term provided by Baidu. Secondly, it is necessary to handle the missing value when the term is out of the trending term list. To achieve the goal of this paper, we collected top 50 trending search terms from Baidu engine and its related news articles hourly for 6 months (from 1st March 2013 to 31th August 2013). Based on the proposed model, we found that the optimal disappearing interval can be 9 h, and using rank 51 for the missing values was the most successful. We conducted evaluations by using 3 months data (from 1st September 2013 to 30th November 2013), and four machine learning techniques where compared to evaluate the most accurate for predicting the popularity rank of trending search terms. Feed Forward Neural Network was achieved 78.81 % the most highest prediction accuracy, and achieved 85.55 % accuracy in ±3 error range.

History

Publication title

Information Technology and Management

Volume

17

Pagination

133-139

ISSN

1385-951X

Department/School

School of Information and Communication Technology

Publisher

Springer New York LLC

Place of publication

United States

Rights statement

Copyright 2015 Springer Science+Business Media New York

Repository Status

  • Restricted

Socio-economic Objectives

Information services not elsewhere classified

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    University Of Tasmania

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