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Found 10 papers, 3 papers with code
Improving One-class Recommendation with Multi-tasking on Various Preference Intensities
However, these efforts assume that all positive signals from implicit feedback reflect a fixed preference intensity, which is not realistic.
Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis
Document representation is the core of many NLP tasks on machine understanding.
Program Machine Policy: Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines
On the other hand, representing RL policies using state machines (Inala et al., 2020) can inductively generalize to long-horizon tasks; however, it struggles to scale up to acquire diverse and complex behaviors.
printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning
Nowadays, modern recommender systems usually leverage textual and visual contents as auxiliary information to predict user preference.
Attentive Graph-based Text-aware Preference Modeling for Top-N Recommendation
Though delivering promising performance for rating prediction, we empirically find that many review-based models cannot perform comparably well on top-N recommendation.
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs
Aiming to produce reinforcement learning (RL) policies that are human-interpretable and can generalize better to novel scenarios, Trivedi et al. (2021) present a method (LEAPS) that first learns a program embedding space to continuously parameterize diverse programs from a pre-generated program dataset, and then searches for a task-solving program in the learned program embedding space when given a task.
Mr. Right: Multimodal Retrieval on Representation of ImaGe witH Text
To address this, we introduce Multimodal Retrieval on Representation of ImaGe witH Text (Mr.
An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese Codex
Word embedding is a modern distributed word representations approach widely used in many natural language processing tasks.
End-to-End Video Question-Answer Generation with Generator-Pretester Network
Furthermore, using our generated QA pairs only on the Video QA task, we can surpass some supervised baselines.
Cross-State Self-Constraint for Feature Generalization in Deep Reinforcement Learning
Representation learning on visualized input is an important yet challenging task for deep reinforcement learning (RL).
