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Catboost-based Framework with Additional User Information for Social Media Popularity Prediction

Published: 15 October 2019 Publication History

Abstract

In this paper, a Catboost-based framework is proposed to predict social media popularity. The framework is constituted by two components: feature representation and Catboost training. In the component of feature representation, numerical features are directly used, while categorical features are converted into numerical features by a method of order target statistics in Catboost. Besides, some additional user information is also tracked to enrich the feature space. In the other component, Catboost is adopted as the regression model which is trained by using post-related, user-related and additional user information. Moreover, to make full use of the dataset for model training, a dataset augmentation strategy based on pseudo labels is proposed. This strategy involves in two-stage training. In the first stage, it trains a first-stage model that is used to label the test set as pseudo labeled. In the next stage, a final model is trained based on the new training set that includes original validation set and the pseudo labeled test set. The proposed method achieves the 2nd place in the leader board of the Grand Challenge of Social Media Prediction.

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    cover image ACM Conferences
    MM '19: Proceedings of the 27th ACM International Conference on Multimedia
    October 2019
    2794 pages
    ISBN:9781450368896
    DOI:10.1145/3343031
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    Published: 15 October 2019

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    Author Tags

    1. catboost
    2. categorical features
    3. social media prediction

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    MM '19 Paper Acceptance Rate 252 of 936 submissions, 27%;
    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    • (2024)MMF: Winning Solution to Social Media Popularity Prediction Challenge 2024Proceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3688997(11445-11449)Online publication date: 28-Oct-2024
    • (2024)Generating surface soil moisture at the 30 m resolution in grape-growing areas based on stacked ensemble learningInternational Journal of Remote Sensing10.1080/01431161.2024.237722845:16(5385-5424)Online publication date: 22-Jul-2024
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