Predicting the Popularity of Micro-videos with Multimodal Variational Encoder-Decoder Framework
Table 3: Comparison of performance between the proposed MMVED-TMP model and several baseline methods.
['Methods', 'nMSE-TMP', 'SRC']
First, deep learning-based methods consistently outperform the machine learning-based ones by a large margin, since for the machine learning-based baselines, the prediction of popularity level at each timestep is made independently by taking the absolute time as an auxiliary 1D-feature, and thus no temporal relationship is utilized in these methods, whereas capturing the dynamic pattern of sequence, on the contrary, is the strength of the RNNs. Second, for the encode-decoder based deep learning methods, those that take the hidden encoding as the initial state of the RNN (MMDED, MMVED-TMP) outperform those use hidden encoding to augment the input at each timestep (CLSTM). The reason could be that by only using the embedding micro-video as an initialization for the hidden state of the RNN, the former methods force the encoder to summarize relevant information from the micro-video features with respect to the popularity trend, blocking the undesired shortcut for the network to base the prediction solely on the absolute time and ignore the encoding. Besides, by concatenating the high dimensional micro-video feature with the absolute time, the CLSTM model dilates the 1D absolute time information, which is shown to be the strong indicator for the popularity trend of the micro-videos in the previous section.