The blowout development of video social platforms has spawned a wide range of social video prediction (SVP) tasks, such as video attractiveness prediction and video sentiment classification. In this paper, we propose to enhance SVP by making synchronous predictions based on temporal and spatial data perspectives and reconciling them to form a consistent predictive view. To this end, we develop a novel multimodal deep learning method named MATSC (modality-awareness- and temporal-spatial-consistency-based neural network). Specifically, MATSC first constructs the temporal predictive view by capturing valuable fine-grained data patterns and generating diverse multimodal representations via the modality-awareness learning strategy. Then, MATSC constructs the spatial predictive view by exploiting diverse modality-wise interactive patterns in fine-grained video clips. Third, MATSC reconciles the heterogeneous temporal and spatial predictive capabilities via a temporal-spatial-consistency learning objective. Empirical results based on three SVP datasets show the outperformance of MATSC over state-of-the-art benchmarks, demonstrating the enhancement effect of synergizing temporal and spatial data views for SVP tasks. 

On the consensus of synchronous temporal and spatial views: A novel multimodal deep learning method for social video prediction

Predicting Short-Term Bike-Sharing Demand at Station Level: A Deep Graph-Based Multi-Task Spatiotemporal Approach

Nowadays, all marketing companies are focusing on advertisements to maximize their sales and profit. Media mix models are used by advertisers to measure how advertisements would effectively, attract and motivate the consumers to buy their brands. However, due to large number of media mix variables, it is difficult to use these media mix models for producing reliable approximation, extending these approximations to infer causation (not as correlation), and parameterizing these models to handle the complex marketing interactions between consumers. In this work, we develop a novel model integrating causal inference analysis with a parameterized quantum stochastic gradient descent model to assess the relation between the marketing advertisements and sales forecasting. The proposed causal aware PQSGD model is experimented on two different media mix variables datasets. The proposed model outperforms the existing quantum approaches (Quantum linear/lasso regression, Quantum decision tree, Quantum LightGBM, Quantum random forest regressor) by achieving the MSE values of 4.36 for Dataset 1 and 19.24 for Dataset 2. 

Causal aware parameterized quantum stochastic gradient descent for analyzing marketing advertisements and sales forecasting

Precise prediction of Multivariate Time Series (MTS) has been playing a pivotal role in numerous kinds of applications. Existing works have made significant efforts to capture temporal tendency and periodical patterns, but they always ignore abrupt variations and heterogeneous/spatial associations of sensory data. In this paper, we develop a dual normalization (dual-norm) based dynamic graph diffusion network (DNGDN) to capture hidden intricate correlations of MTS data for temporal prediction. Specifically, we design time series decomposition and dual-norm mechanism to learn the latent dependencies and alleviate the adverse effect of abnormal MTS data. Furthermore, a dynamic graph diffusion network is adopted for adaptively exploring the spatial correlations among variables. Extensive experiments are performed on 3 real world experimental datasets with 8 representative baselines for temporal prediction. The performances of DNGDN outperforms all baselines with at least 4% lower MAPE over all datasets. 

Dual-norm based dynamic graph diffusion network for temporal prediction

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Dual-norm based dynamic graph diffusion network for temporal prediction