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Chinese trending search terms popularity rank prediction

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Abstract

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.

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Acknowledgments

This study was supported by Asian Office of Aerospace Research and Development (AOARD).

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Authors and Affiliations

  1. School of Engineering and ICT, University of Tasmania, Sandy Bay, TAS, Australia

    Soyeon Caren Han, Yulu Liang, Hyunsuk Chung & Byeong Ho Kang

  2. Institute of School Safety Research, Sungshin Women’s University, 2, Bomun-ro 34da-gil, Seongbuk-gu, Seoul, South Korea

    Hyejin Kim

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  1. Soyeon Caren Han
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  2. Yulu Liang
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Correspondence to Hyejin Kim.

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Han, S.C., Liang, Y., Chung, H. et al. Chinese trending search terms popularity rank prediction. Inf Technol Manag 17, 133–139 (2016). https://doi.org/10.1007/s10799-015-0238-0

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  • DOI: https://doi.org/10.1007/s10799-015-0238-0

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