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Found 9 papers, 5 papers with code
Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction
Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on predicted helpfulness scores and has been widely applied in e-commerce via presenting customers with useful reviews.
Improving the Inference of Topic Models via Infinite Latent State Replications
In text mining, topic models are a type of probabilistic generative models for inferring latent semantic topics from text corpus.
Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions
To overcome the aforementioned issues, we propose Multimodal Contrastive Learning for Multimodal Review Helpfulness Prediction (MRHP) problem, concentrating on mutual information between input modalities to explicitly elaborate cross-modal relations.
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning
With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP), which aims to sort product reviews according to the predicted helpfulness scores has become a research hotspot.
Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews
In addition, we also devise an intra-review coherent reasoning module to identify the coherence between the text content and images of the review, which is a piece of strong evidence for review helpfulness prediction.
DeepMnemonic: Password Mnemonic Generation via Deep Attentive Encoder-Decoder Model
Moreover, the user study further validates that the generated mnemonic sentences by DeepMnemonic are useful in helping users memorize strong passwords.
Heron Inference for Bayesian Graphical Models
Bayesian graphical models have been shown to be a powerful tool for discovering uncertainty and causal structure from real-world data in many application fields.
