Search Results for author:
Found 10 papers, 5 papers with code
Transferable Fairness for Cold-Start Recommendation
The proposed method is able to transfer the knowledge of a fair model learned from the source users to the target users with the hope of improving the recommendation performance and keeping the fairness property on the target users.
Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)
Experiments on different datasets show that the adaptive architecture assembled by MANAS outperforms static global architectures.
Fairness in Recommendation: Foundations, Methods and Applications
It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems.
Graph Collaborative Reasoning
Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering.
Personalized Counterfactual Fairness in Recommendation
Therefore, it is important to provide personalized fair recommendations for users to satisfy their personalized fairness demands.
User-oriented Fairness in Recommendation
To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics.
Generate Natural Language Explanations for Recommendation
Motivated by this problem, we propose to generate free-text natural language explanations for personalized recommendation.
Neural Logic Reasoning
Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs.
Neural Collaborative Reasoning
Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i. e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so that a user embedding can be matched with relevant item embeddings using designed or learned similarity functions.
Neural Logic Networks
The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of logical reasoning.
