Search Results for author:
Found 11 papers, 4 papers with code
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph.
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user.
Towards Long-term Fairness in Recommendation
We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.
Faithfully Explainable Recommendation via Neural Logic Reasoning
Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process.
Waterfall Bandits: Learning to Sell Ads Online
We design an online learning algorithm for solving this problem, which interleaves learning and optimization, and prove that this algorithm has sublinear regret.
ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal.
Fairness-Aware Explainable Recommendation over Knowledge Graphs
There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems.
Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation
Recent work on recommender systems has considered external knowledge graphs as valuable sources of information, not only to produce better recommendations but also to provide explanations of why the recommended items were chosen.
Enhanced MRI Reconstruction Network using Neural Architecture Search
The accurate reconstruction of under-sampled magnetic resonance imaging (MRI) data using modern deep learning technology, requires significant effort to design the necessary complex neural network architectures.
COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce
In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE.
A Survey on Trustworthy Recommender Systems
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process.
