Predicting the Popularity of Micro-videos with Multimodal Variational Encoder-Decoder Framework
Table 2: Comparison of performance between the proposed MMVED-REG model and several SoTA methods.
['Methods', 'nMSE']
TRLMVR further improves TMALL by adding a low-rank constraint of hidden space to the multi-view learning objective such that the insignificant components of the feature spaces are removed, which leads to substantial improvements compared to TMALL. However, in TRLMVR, the low-rank constraint is hardwired and universal for all samples, such that samples with different noise levels could not be properly distinguished. Besides, their hidden representation of TRLMVR is inherently deterministic. On the other hand, in our MMVED-REG model, we first preserve the uncertainty of popularity mapping by modeling the hidden representation as a Gaussian variable. In addition, for each micro-video, the mean (semantic part) and std (uncertainty and noise part) of its Gaussian embedding are independently inferred through the PoE inference network. Furthermore, an information bottleneck constraint is set upon the Gaussian embeddings, such that the relevant information obscured by the information-rich but noisy micro-video contents can be actively learned to be extracted into the hidden representation with the guidance of the training popularity groundtruth. Therefore, our method achieves the best results among the SoTA methods.