Xuemeng Song
    Assistant Professor
    Department of Computer Science and Technology
    Shandong University
    Address: Shandong University, Jimo, Tsingtao, China, 266237
    Email: sxmustc at gmail dot com
News Biography Publications Research
News
Call for papers: Information Processing and Management Special Issue on Deep Learning for Multi-modal Social Media Analysis and Applications
Opening for master students: I am recruiting self-motivated master students to collaborate on research domains such as information retrieval and social media analysis. Students with Bachelor's degrees in CS or other related areas (e.g, Mathematics, Physics, EE, etc.) are all welcome. (Enrollment of 2020 Sep).
Biography
Xuemeng Song is an Assistant Professor in Shandong University. She got her PhD degree from National University of Singapore (NUS) and her Bacherlor degree from Universitiy of Science and Technology of China (USTC) in 2016 and 2012, respectively. Her research interests are information retrieval and social media analysis. She has published several papers in the top venues, such as SIGIR, MM, IJCAI, AAAI, TOIS and TNNLS. In addition, she has served as reviewers for many top conferences and journals, such as TKDD, TMM, ICMR, and MMM.
 
Publications
  1. Prototype-guided Attribute-wise Interpretable Scheme for Clothing Matching.
    Xianjing Han, Xuemeng Song, Jianhua Yin, Yinglong Wang and Liqiang Nie. In SIGIR, 2019. (Full paper)
  2. Supervised Hierarchical Cross-Modal Hashing.
    Changchang Sun, Xuemeng Song, Fuli Feng, Wayne Xin Zhao, Hao Zhang and Liqiang Nie. In SIGIR, 2019. (Full paper)
  3. User Attention-guided Multimodal Dialog Systems.
    Chen Cui, Wenjie Wang, Xuemeng Song, Minlie Huang, Xin-Shun Xu and Liqiang Nie. In SIGIR, 2019. (Full paper)
  4. Neural Compatibility Modeling with Attentive Knowledge Distillation.
    Xuemeng Song, Fuli Feng, Xianjing Han, Xin Yang, Liqiang Nie, Wei Liu. In SIGIR, 2018. (Full paper) Pdf
  5. A Personal Privacy Preserving Framework: I Let You Know Who Can See What.
    Xuemeng Song, Xiang Wang, Liqiang Nie, Xiangnan He, Zhumin Chen, Wei Liu. In SIGIR, 2018. (Full paper) Pdf
  6. Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection.
    Xiao Dong, Lei Zhu, Xuemeng Song, Jingjing Li, Zhiyong Cheng. In IJCAI, 2018. (Full paper) Pdf
  7. SDMCH: Supervised Discrete Manifold-Embedded Cross-Modal Hashing.
    Xin Luo, Xiao-Ya Yin, Liqiang Nie, Xuemeng Song, Yongxin Wang, Xin-Shun Xu. In IJCAI, 2018. (Full paper) Pdf
  8. A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction.
    Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, Mohan S. Kankanhalli. In IJCAI, 2018. (Full paper) Pdf
  9. Scalable graph based non-negative multi-view embedding for image ranking.
    Shuhan Qi, Xuan Wang, Xi Zhang, Xuemeng Song, Zoe L. Jiang. Neurocomputing, 274: 29-36, 2018. Pdf
  10. Venue Prediction for Social Images by Exploiting Rich Temporal Patterns in LBSNs.
    Jingyuan Chen, Xiangnan He, Xuemeng Song, Hanwang Zhang, Liqiang Nie, Tat-Seng Chua. In MMM, 2018. (Full paper) Pdf
  11. NeuroStylist: Neural Compatibility Modeling for Clothing Matching.
    Xuemeng Song, Fuli Feng, Jinhuan Liu, Zekun Li, Liqiang Nie, Jun Ma. In MM, 2017. (Full paper) Pdf
  12. Unifying virtual and physical worlds: Learning toward local and global consistency.
    Xiang Wang, Liqiang Nie, Xuemeng Song, Dongxiang Zhang, Tat-Seng Chua. ACM Transactions on Information Systems (TOIS), 2017. Pdf
  13. Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer's Disease.
    Liqiang Nie, Luming Zhang, Lei Meng, Xuemeng Song, Xiaojun Chang, Xuelong Li. IEEE Transactions on Neural Network Learning System (TNNLS), 2017. Pdf
  14. Micro Tells Macro: Predicting the Popularity of Micro-Videos via a Transductive Model.
    Jingyuan Chen, Xuemeng Song, Liqiang Nie, Xiang Wang, Hanwang Zhang, Tat-Seng Chua. In MM, 2016. (Full paper) Pdf
  15. Learning from Multiple Social Networks.
    Liqiang Nie, Xuemeng Song, Tat-Seng Chua. Synthesis Lectures on Information Concepts, Retrieval, and Services. Morgan & Claypool Publishers, 2016. Pdf
  16. Volunteerism Tendency Prediction via Harvesting Multiple Social Networks.
    Xuemeng Song, Zhao-Yan Ming, Liqiang Nie, Yi-Liang Zhao, Tat-Seng Chua. ACM Transactions on Information Systems (TOIS), 2016. Pdf
  17. Fusing Social Networks with Deep Learning for Volunteerism Tendency Prediction.
    Yongpo Jia, Xuemeng Song, Jingbo Zhou, Li Liu, Liqiang Nie, David S. Rosenblum. In AAAI, 2016. (Full paper) Pdf
  18. Interest Inference via Structure-Constrained Multi-Source Multi-Task Learning.
    Xuemeng Song, Liqiang Nie, Luming Zhang, Maofu Liu, Tat-Seng Chua. In IJCAI, 2015. (Full paper) Pdf
  19. Multiple Social Network Learning and Its Application in Volunteerism Tendency Prediction.
    Xuemeng Song, Liqiang Nie, Luming Zhang, Mohammad Akbari, Tat-Seng Chua. In SIGIR, 2015. (Full paper) Pdf
  20. Enrichment of user profiles across multiple online social networks for volunteerism matching for social enterprise. Xuemeng Song. In SIGIR, 2014. Pdf
Research

Fashion Analysis towards Clothing Matching

According to the Goldman Sachs, the 2016 online retail market of China for fashion products, including apparel, footwear, and accessories, has reached 187.5 billion US dollars, which demonstrates people’s great demand for clothing. In fact, apart from physiological needs, people also have esteem needs of clothes as dressing properly is of importance in daily life. As each outfit usually involves multiple complementary items (e.g., tops, bottoms, and shoes), the key to a proper outfit lies in the harmonious clothing matching to a great extent. However, not everyone is a naturalborn fashion stylist, which makes choosing the matching clothes a tedious and even annoying daily routine. It thus deserves our attention to develop an effective clothing matching scheme to help people figure out the suitable match for a given item and make a harmonious outfit.

User Profiling across Multiple Social Networks

User profiling, which aims to infer users' unobservable information based on observable information such as individual's behavior or utterances, is the basis for many applications, such as personalized recommendation, and expert finding. Traditional user profiling conducted with traditional medium, such as document records, is always hindered by the limited data sources. Recent years, the proliferation of social media has opened new opportunities for user profiling. Moreover, as different social networks provide different services, increasing number of people are involved in multiple social networks. Different aspects can be revealed by different social networks. Therefore, to comprehensively learn users' profiles, it is time to shift from a single social network to multiple social networks.

Privacy Preserving in Social Media

The boom of social networks has given rise to a large volume of user-generated contents (UGCs), most of which are freely and publicly available. The potential of using the rich set of UGCs to study people's personal attributes and personalized applications has been widely validated. Despite its value, UGCs can also place users at high privacy risks, which thus far remains largely untapped. Privacy is defined as the individual's ability to control what information is disclosed, to whom, when and under what circumstances. As people and information both play significant roles, privacy has been elaborated as a boundary regulation process, where individuals regulate interaction with others by altering the openness degree of themselves to others. Therefore, we aim to reduce users' privacy risks on social networks by answering the question of Who Can See What.