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Found 10 papers, 3 papers with code
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction
Specifically, DRK adopts a logic-aware inference module to ease the word-overlap confusion, which introduces a logic rule to constrain the inference process, thereby avoiding the adverse effect of shallow text features.
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure
Then, we introduce a penalty to items with high exposure probability to avoid the overestimation of user preference for biased samples.
Disentangled Variational Auto-encoder Enhanced by Counterfactual Data for Debiasing Recommendation
In specific, DB-VAE first extracts two types of extreme items only affected by a single bias based on the collier theory, which are respectively employed to learn the latent representation of corresponding biases, thereby realizing the bias decoupling.
MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection
Event detection (ED) is aimed to identify the key trigger words in unstructured text and predict the event types accordingly.
Heterogeneous Graph Neural Networks to Predict What Happen Next
Existing work cannot well represent the heterogeneous relations and capture the discontinuous event segments that are common in the event chain.
Star Graph Neural Networks for Session-based Recommendation
The proposed SGNN-HN applies a star graph neural network (SGNN) to model the complex transition relationship between items in an ongoing session.
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Session-Based Recommendations
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Rethinking Item Importance in Session-based Recommendation
We employ a modified self-attention mechanism to estimate item importance in a session, which is then used to predict user's long-term preference.
Pre-train, Interact, Fine-tune: A Novel Interaction Representation for Text Classification
We introduce the concept of interaction and propose a two-perspective interaction representation, that encapsulates a local and a global interaction representation.
Improving End-to-End Sequential Recommendations with Intent-aware Diversification
Then, we design an Intent-aware Diversity Promoting (IDP) loss to supervise the learning of the IIM module and force the model to take recommendation diversity into consideration during training.
Joint Neural Collaborative Filtering for Recommender Systems
Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix.
