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Found 9 papers, 3 papers with code
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.
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.
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.
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.
Offline Evaluation of Ranking Policies with Click Models
We analyze our estimators and prove that they are more efficient than the estimators that do not use the structure of the click model, under the assumption that the click model holds.
Stochastic Low-Rank Bandits
Many problems in computer vision and recommender systems involve low-rank matrices.
Graphical Model Sketch
Graphical models are a popular approach to modeling structured data but they are unsuitable for high-cardinality variables.
Adaptive Submodular Maximization in Bandit Setting
Maximization of submodular functions has wide applications in machine learning and artificial intelligence.
Node Classification in Social Networks
When dealing with large graphs, such as those that arise in the context of online social networks, a subset of nodes may be labeled.
Social and Information Networks Physics and Society
