TY - GEN
T1 - Combining multiple features for image popularity prediction in social media
AU - Wang, Wen
AU - Zhang, Wei
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/10/23
Y1 - 2017/10/23
N2 - Popularity prediction, aiming at predicting target items' total interactions with users, is a very significant type of problem and has attracted a lot of attention in recent years. It can benefiit a lot of real applications, such as cold-start recommendation [8] and online advertising [4]. The Social Media Prediction Task-1 (SMP-T1) of the ACM Multimedia 2017 Grand Challenge is designed to predict popularity of photos published by users in social media. In this paper, we introduce the method adopted in this contest detailedly. It is mainly based on carefully designed features and selected regression models. We demonstrate the effectiveness of each feature proposed for this task via univariate and ablation tests by employing different models. Based on those results, we further integrate the verified useful features with the best-performing regression model to obtain final prediction results. We participated this contest with the team name "heihei" and ranked in the second place in the final ranking list.
AB - Popularity prediction, aiming at predicting target items' total interactions with users, is a very significant type of problem and has attracted a lot of attention in recent years. It can benefiit a lot of real applications, such as cold-start recommendation [8] and online advertising [4]. The Social Media Prediction Task-1 (SMP-T1) of the ACM Multimedia 2017 Grand Challenge is designed to predict popularity of photos published by users in social media. In this paper, we introduce the method adopted in this contest detailedly. It is mainly based on carefully designed features and selected regression models. We demonstrate the effectiveness of each feature proposed for this task via univariate and ablation tests by employing different models. Based on those results, we further integrate the verified useful features with the best-performing regression model to obtain final prediction results. We participated this contest with the team name "heihei" and ranked in the second place in the final ranking list.
KW - ACM multimedia 2017 grand challenge
KW - Feature engineering
KW - GBRT
KW - Social media prediction
UR - https://www.scopus.com/pages/publications/85035215909
U2 - 10.1145/3123266.3127900
DO - 10.1145/3123266.3127900
M3 - 会议稿件
AN - SCOPUS:85035215909
T3 - MM 2017 - Proceedings of the 2017 ACM Multimedia Conference
SP - 1901
EP - 1905
BT - MM 2017 - Proceedings of the 2017 ACM Multimedia Conference
PB - Association for Computing Machinery, Inc
T2 - 25th ACM International Conference on Multimedia, MM 2017
Y2 - 23 October 2017 through 27 October 2017
ER -