DTCN

This is the implementation of our paper "Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks".

Dataset

To successfully test performance, we created TPIC Dataset, a temporal popularity image collection dataset.

Overview

Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales.

Environment

The code is pure python. Keras is chosen to be the deep learning library here. Environment is configured by Anaconda. The environment file is saved as "environment.yml".

• Ubuntu 16.04
• Python 2.7
• Cuda 10.0
• cudnn 7.6.5

Setup

conda env create -f environment.yml

Prequisition

• Clone the repository to your local machine
• Acquire relevant dataset
• Extract the image feature with ResNet (2048 dims)
• Run script by seeing example.

Usage

DATA_HOME=test_data/TRIM_DATA
KERAS_BACKEND=theano \
THEANO_FLAGS='mode=FAST_RUN,device=cuda0,nvcc.fastmath=True,optimizer=fast_run' \
python main.py \
-feature_path $DATA_HOME/USER_20W_SORTED_BY_TIME.txt \
-meta_path $DATA_HOME/ResNet_20W_2048_SORTED_BY_TIME.txt \
-label_path $DATA_HOME/LABEL_20W_SORTED_BY_TIME.txt \
-algorithm SHARED_DTCN \
-nb_epoch 1000 \
-start_cross_validation 2 \
-total_cross_validation 3 \
-identifier_path $DATA_HOME/USERID_20W_SORTED_BY_TIME.txt \
-timestamps_path $DATA_HOME/TIMESTAMP_20W_SORTED_BY_TIME.txt \
-visual_mlp_enabled y \
-timestep 10 \
-time_align y \
-time_dis_con continue \
-time_context_length 18 \
-time_unit_metric hour \
-discrete_time_start_offset 2 \
-discrete_time_unit 4 \
-train_set_partial 9 \
-merge_mode concat \
-dual_time_align n \
-time_weight_mode time_flag \
-dual_lstm n

Citation

@inproceedings{Wu2017DTCN,
  title={Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks},
  author={Wu, Bo and Cheng, Wen-Huang and Zhang, Yongdong and Qiushi, Huang and Jintao, Li and Mei, Tao},
  booktitle={IJCAI},
  year={2017},
  location = {Melbourne, Australia}}

Please concat us (social.media.prediction@gmail.com) if you have further questions or cooporations