Runze Wu

Runze Wu
NetEase · Fuxi AI Lab

Doctor of Philosophy

About

40
Publications
3,300
Reads
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199
Citations
Introduction
Runze Wu currently works at Fuxi AI Lab, NetEase Inc, Hangzhou. Research interests include user behavior modeling, temporal prediciton and causal inferrence.
Additional affiliations
December 2018 - present
NetEase
Position
  • Researcher
Description
  • I currently work as the leader of User Profiling Group of Fuxi AI Lab, NetEase Inc. My interests cover a wide range of user modeling, personalization, behavior analysis, and AI for novel applications. My colleagues and I have published dozens of top-tier conference/journal papers like KDD, AAAI, CIKM, TKDE, TKDD, and TC.
Education
January 2016 - January 2017
University of Technology Sydney
Field of study
  • Data Mining
September 2013 - November 2018
University of Science and Technology of China
Field of study
  • Computer Science, Data Mining, Machine Learning
September 2013 - November 2018
University of Science and Technology of China
Field of study
  • Data Mining, Machine Learning, Cognitive Modeling

Publications

Publications (40)
Conference Paper
Self-attention models have achieved state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user-item interactions. However, they rely on positional embeddings to retain the sequential information, which may break the semantics of item embeddings. In addition, most existing works assume that suc...
Article
In data mining and machine learning, it is commonly assumed that training and test data share the same population distribution. However, this assumption is often violated in practice because of the sample selection bias, which might induce the distribution shift from training data to test data. Such a model-agnostic distribution shift usually leads...
Article
Market popularity prediction has always been a hot research topic, such as sales prediction and crowdfunding prediction. Most of these studies put the perspective on isolated markets, relying on the knowledge of certain market to maximize the prediction performance. However, these market-specific approaches are restricted by the knowledge limitatio...
Article
As an independent social and economic entity, game servers plays a dominant role in building a stable, living, and attractive virtual world in massive multi-player online role-playing games (MMORPGs). We propose and implement a novel intelligent decision support system for server merge (SM) for maintaining the game ecology at the macro level. The s...
Article
Guild is the most important long-term virtual community and emotional bond in massively multiplayer online role-playing games (MMORPGs). It matters a lot to the player retention and game ecology how the guilds are going, e.g., healthy or not. The main challenge now is to characterize and predict the guild health in a quantitative, dynamic, and mult...
Chapter
Game bots are automated programs that assist cheating players in obtaining huge superiority in Massively Multiplayer Online Role-Playing Games (MMORPGs), which has led to an imbalance in the gaming ecosystem and a collapse of interest among normal players. Game bot detection aims to identify cheating behaviors to ensure fair competition for MMORPGs...
Article
In recent years, advances in Graph Convolutional Networks (GCNs) have given new insights into the development of social recommendation. However, many existing GCN-based social recommendation methods often directly apply GCN to capture user-item and user-user interactions, which probably have two main limitations: (a) Due to the power-law property o...
Preprint
Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models borrow the success of Graph Neural Networks (GNN), and iteratively perform neighborhood aggregation to propagate the collaborative...
Preprint
Full-text available
Self-attention models have achieved state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user-item interactions. However, they rely on positional embeddings to retain the sequential information, which may break the semantics of item embeddings. In addition, most existing works assume that suc...
Preprint
Density-based and classification-based methods have ruled unsupervised anomaly detection in recent years, while reconstruction-based methods are rarely mentioned for the poor reconstruction ability and low performance. However, the latter requires no costly extra training samples for the unsupervised training that is more practical, so this paper f...
Preprint
Reinforcement learning based recommender systems (RL-based RS) aims at learning a good policy from a batch of collected data, with casting sequential recommendation to multi-step decision-making tasks. However, current RL-based RS benchmarks commonly have a large reality gap, because they involve artificial RL datasets or semi-simulated RS datasets...
Article
Social relationships are the basis for communication and collaboration between players in many online games. In this paper, we propose a machine learning-based approach to model the relationship between players and guilds in online games. Our approach combines deep learning techniques with useful prior expert knowledge, where the core component is...
Preprint
Full-text available
Instrumental variables (IVs), sources of treatment randomization that are conditionally independent of the outcome, play an important role in causal inference with unobserved confounders. However, the existing IV-based counterfactual prediction methods need well-predefined IVs, while it's an art rather than science to find valid IVs in many real-wo...
Preprint
Full-text available
In business domains, \textit{bundling} is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers. Existing recommender systems mostly focus on recommending individual items that users may be interested in. In this paper, we target at a practical but less explor...
Preprint
Full-text available
In recent years, there are great interests as well as challenges in applying reinforcement learning (RL) to recommendation systems (RS). In this paper, we summarize three key practical challenges of large-scale RL-based recommender systems: massive state and action spaces, high-variance environment, and the unspecific reward setting in recommendati...
Conference Paper
Full-text available
Online gaming is a multi-billion dollar industry that entertains a large, global population. However, one unfortunate phenomenon known as real money trading harms the competition and the fun. Real money trading is an interesting economic activity used to exchange assets in a virtual world with real world currencies, leading to imbalance of game eco...
Article
Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been widely used in recommender systems. The literature has reported that matrix factorization methods often produce superior accuracy of rating prediction in recommender systems. However, existing matrix factorization methods rarely consider confidence of the...
Article
Full-text available
Recent decades have witnessed the rapid growth of educational data mining (EDM), which aims at automatically extracting valuable information from large repositories of data generated by or related to people’s learning activities in educational settings. One of the key EDM tasks is cognitive modelling with examination data, and cognitive modelling t...
Conference Paper
Full-text available
Diagnosing students' knowledge proficiency, i.e., the mastery degrees of a particular knowledge point in exercises, is a crucial issue for numerous educational applications, e.g., targeted knowledge training and exercise recommendation. Educational theories have converged that students learn and forget knowledge from time to time. Thus, it is neces...
Conference Paper
Recent decades have witnessed the rapid growth of intelligent tutoring systems (ITS), in which personalized adaptive techniques are successfully employed to improve the learning of each individual student. However, the problem of using cognitive analysis to distill the knowledge and gaming factor from students learning history is still underexplore...
Article
Personalized question recommendation for students is a significant research direction in the domain of intelligent education. Current studies depend on either collaborative filtering based methods or use the cognitive diagnosis models. Unfortunately, collaborative filterings ignore the knowledge states (e.g. skill proficiency) of students and cogni...
Conference Paper
With a number of students, the purpose of collaborative learning is to assign these students to the right teams so that the promotion of skills of each team member can be facilitated. Although some team formation solutions have been proposed, the problem of extracting more effective features to describe the skill proficiency of students for better...

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