1.5k citations · 5.2k across the 61 of their papers we have counts for
18 papers · 1 filter
Generative Inverse Deep Reinforcement Learning for Online Recommendation
Xiaocong Chen, Lina Yao, Aixin Sun +3
Deep reinforcement learning enables an agent to capture user's interest through interactions with the environment dynamically. It has attracted great interest in the recommendation…
TRec: Sequential Recommender Based On Latent Item Trend Information
Ye Tao, Can Wang, Lina Yao +2
Recommendation system plays an important role in online web applications. Sequential recommender further models user short-term preference through exploiting information from lates…
Recommender Systems for the Internet of Things: A Survey
May Altulyan, Lina Yao, Xianzhi Wang +3
Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT). Traditional recommender systems fail to exploit ever-growing, dyna…
MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
Manqing Dong, Feng Yuan, Lina Yao +2
A common challenge for most current recommender systems is the cold-start problem. Due to the lack of user-item interactions, the fine-tuned recommender systems are unable to handl…
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
Xiaocong Chen, Chaoran Huang, Lina Yao +3
Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous…
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
Manqing Dong, Feng Yuan, Lina Yao +3
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation r…
Quaternion Collaborative Filtering for Recommendation
Shuai Zhang, Lina Yao, Lucas Vinh Tran +2
This paper proposes Quaternion Collaborative Filtering (QCF), a novel representation learning method for recommendation. Our proposed QCF relies on and exploits computation with Qu…
DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
Shuai Zhang, Yi Tay, Lina Yao +2
Deep learning based recommender systems have been extensively explored in recent years. However, the large number of models proposed each year poses a big challenge for both resear…
Software Expert Discovery via Knowledge Domain Embeddings in a Collaborative Network
Chaoran Huang, Lina Yao, Xianzhi Wang +2
Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considerin…
Next Item Recommendation with Self-Attention
Shuai Zhang, Yi Tay, Lina Yao +1
In this paper, we propose a novel sequence-aware recommendation model. Our model utilizes self-attention mechanism to infer the item-item relationship from user's historical intera…
Adversarial Collaborative Auto-encoder for Top-N Recommendation
Feng Yuan, Lina Yao, Boualem Benatallah
During the past decade, model-based recommendation methods have evolved from latent factor models to neural network-based models. Most of these techniques mainly focus on improving…
Expert Recommendation via Tensor Factorization with Regularizing Hierarchical Topical Relationships
Chaoran Huang, Lina Yao, Xianzhi Wang +3
Knowledge acquisition and exchange are generally crucial yet costly for both businesses and individuals, especially when the knowledge concerns various areas. Question Answering Co…
NeuRec: On Nonlinear Transformation for Personalized Ranking
Shuai Zhang, Lina Yao, Aixin Sun +3
Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relat…
Metric Factorization: Recommendation beyond Matrix Factorization
Shuai Zhang, Lina Yao, Yi Tay +3
In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations. Nevertheless, the dot p…
Deep Learning based Recommender System: A Survey and New Perspectives
Shuai Zhang, Lina Yao, Aixin Sun +1
With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems ca…
Hybrid Collaborative Recommendation via Semi-AutoEncoder
Shuai Zhang, Lina Yao, Xiwei Xu +2
In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as p…
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders
Shuai Zhang, Lina Yao, Xiwei Xu
Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matri…
Dynamic Intention-Aware Recommendation System
Shuai Zhang, Lina Yao
Recommender systems have been actively and extensively studied over past decades. In the meanwhile, the boom of Big Data is driving fundamental changes in the development of recomm…