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- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
- WWW
- 2017
In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on… (More)
- Xiangnan He, Hanwang Zhang, Min-Yen Kan, Tat-Seng Chua
- SIGIR
- 2016
This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due… (More)
- Xiangnan He, Tat-Seng Chua
- SIGIR
- 2017
Many predictive tasks of web applications need to model categorical variables, such as user IDs and demographics like genders and occupations. To apply standard machine learning techniques, these… (More)
- Xiangnan He, Tao Chen, Min-Yen Kan, Xiao Chen
- CIKM
- 2015
Most existing collaborative filtering techniques have focused on modeling the binary relation of users to items by extracting from user ratings. Aside from users' ratings, their affiliated reviews… (More)
- Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, Tat-Seng Chua
- SIGIR
- 2017
Multimedia content is dominating today's Web information. The nature of multimedia user-item interactions is 1/0 binary implicit feedback (e.g., photo likes, video views, song downloads, etc.), which… (More)
- Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, Tat-Seng Chua
- IJCAI
- 2017
Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be… (More)
- Xiangnan He, Min-Yen Kan, Peichu Xie, Xiao Chen
- WWW
- 2014
Clustering Web 2.0 items (i.e., web resources like videos, images) into semantic groups benefits many applications, such as organizing items, generating meaningful tags and improving web search. In… (More)
- Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan, Tat-Seng Chua
- SIGIR
- 2016
We address the efficiency problem of Collaborative Filtering (CF) by hashing users and items as latent vectors in the form of binary codes, so that user-item affinity can be efficiently calculated in… (More)
- Lizi Liao, Xiangnan He, Hanwang Zhang, Tat-Seng Chua
- IEEE Transactions on Knowledge and Data…
- 2018
Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing… (More)
- Yiping Jin, Min-Yen Kan, Jun-Ping Ng, Xiangnan He
- EMNLP
- 2013
This paper presents DefMiner, a supervised sequence labeling system that identifies scientific terms and their accompanying definitions. DefMiner achieves 85% F1 on a Wikipedia benchmark corpus,… (More)