Search Results for author:
Found 13 papers, 2 papers with code
Boosting Factorization Machines via Saliency-Guided Mixup
Second, considering that MixFM may generate redundant or even detrimental instances, we further put forward a novel Factorization Machine powered by Saliency-guided Mixup (denoted as SMFM).
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation
To improve adaptation efficiency, we learn to recover the user policy and reward from only a few interactions via an inverse reinforcement learning method to assist a meta-level recommendation agent.
Model-based Reinforcement Learning
Recommendation Systems
+2
Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop
In this journal version, we propose a novel interactive and closed-loop architecture to simultaneously model interactive reinforcement learning (IRL) and decision tree feedback (DTF).
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning
Motivated by such a computational dilemma, this study is to develop a novel feature space navigation method.
Explainable Recommender Systems via Resolving Learning Representations
Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge.
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning
With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experience, and boost business revenue.
DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation
Recently, we propose a preliminary work of a neural influence diffusion network (i. e., DiffNet) for social recommendation (Diffnet), which models the recursive social diffusion process to capture the higher-order relationships for each user.
Binarized Collaborative Filtering with Distilling Graph Convolutional Networks
Then we distill the ranking information derived from GCN into binarized collaborative filtering, which makes use of binary representation to improve the efficiency of online recommendation.
Personalized Multimedia Item and Key Frame Recommendation
We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models would fail without any users' fine-grained image behavior.
Micro- and Macro-Level Churn Analysis of Large-Scale Mobile Games
In this paper, we present the first large-scale churn analysis for mobile games that supports both micro-level churn prediction and macro-level churn ranking.
A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games
To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions.
A Context-aware Attention Network for Interactive Question Answering
Employing these attention mechanisms, our model accurately understands when it can output an answer or when it requires generating a supplementary question for additional input depending on different contexts.
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.
