A Hierarchical Attention-based Contrastive Learning Method for Micro Video Popularity Prediction

Micro videos popularity prediction (MVPP) has recently attracted widespread research interests given the increasing prevalence of video-based social platforms. However, previous studies have overlooked the unique patterns between popular and unpopular videos and the interactions between asynchronous features different data dimensions. To address this, we propose a novel hierarchical attention contrastive learning method named HACL, which extracts explainable representation features, learns their asynchronous interactions from both temporal and spatial levels, and separates the positive and negative embeddings identities. This reveals video popularity in a contrastive and interrelated view, and thus can be responsible for a better MVPP. Dual neural networks account for separate positive and negative patterns via contrastive learning. To obtain the temporal-wise interaction coefficients, we propose a Hadamard-product based attention approach to optimize the trainable attention-map matrices. Results from our experiments on a TikTok micro video dataset show that HACL outperforms benchmarks and provides insightful managerial implications

Complementary or Substitutive? Leveraging Text-image Interactions for Review Helpfulness Prediction

Extensive research have shown the significant influence of textual contents on review helpfulness prediction, however, the review images draw little attention. Actually, the information conveyed in the review images can be either additional information (complementation effect) or similar information (substitution effect) in contrast with the textual review information. We propose a novel multimodal deep learning method to better leverage the online review texts and images and capture such interaction effect between them for their helpfulness prediction. The method firstly extracts the multimodal features using the pre-trained deep learning models and then feeds into the LSTM and attention units to learn the sequential dependency relation and importance weights. We formulize the complementation-substitution based text-image interaction effects into the loss function. Empirical experiment results on a large-scale online review dataset show the superiority of our method in terms of both prediction performance and representation learning performance

Being Sagacious towards Proliferated Post-Purchase Sharing: A Novel Disclosure Pattern-Wise Helpful Online Reviews Extraction Method

The proliferation of social media platforms flourishes research on helpful online reviews. Prior studies have ubiquitously taken subjective indicators to measure online review helpfulness, such as the voted reviews and reviews with an emotional tendency. By highlighting helpful reviews, researchers strive to extricate consumers from the explosive growth amount of post-purchase information. In this study, we theoretically reformulate the consumer-oriented online review helpfulness as three indicators, including effectiveness (i.e., product-specific), representativeness or objectivity (i.e., identical distribution with original review set), and semantic diversity (for personalized information demand). Moreover, we design a novel disclosure pattern-wise method to coordinate the three indicators for enhancing helpful review extraction. Experiments on more than 2 million of hotel reviews manifest the superiority of our proposed method for balancing the trade-off among different review helpfulness indicators, in contrast to conventional helpful review extraction methods