IFUP: Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization

The international workshop IFUP 2018 will be co-located with WSDM 2018: The 11th ACM International Conference on Web Search and Data Mining, to be held in Los Angeles, California during Feb 6-8, 2018.

Call for Papers

Real-world systems accumulate large-scale and various kinds of multimodal information rapidly, including but not limited to text, image, video, audio, social relations, meta data, etc. Such information has been incorporated in many recommendation models and systems to promote the performance and user experience, giving rise to some of the recent and/or classical topics in recommender system, such as review-based recommendation, image-based recommendation, explainable recommendation, deep learning for recommendation, POI recommendation, video/music recommendation, and many others.

Integrating multimodal side information is not a trivial task, because information may be either homogenous or heterogeneous, which requires more advanced method for information fusion and alignment. Besides, different information may play distinct roles for different domains, users, and tasks, and the availability of new information source may even lead to completely new recommendation research tasks. As a result, it needs significant efforts from both the research community and industry to promote the recommendation system research and application in face of the various information sources.

IFUP 2018 aims to provide a dedicated forum for discussing open problems, challenges and innovative research approaches in fusing multi-dimensional information for user modelling and recommender systems. The major goal of this workshop is to promote advanced recommendation solutions that can be easily and readily deployed to meet industrial demands for personalized recommendation.

Topics of the workshop include but are not limited to:

  • Text-based Recommendation
    • Sentiment Analysis for Recommendation
    • Topic Modeling Approaches to Recommendation
    • Deep Text Modeling for Recommendation
    • Natural Languange Processing for Recommendation
    • Question Anwering for Recommendation
  • Image-based Recommendation
    • Deep/Shallow Image Modeling for Recommendation
    • Image Recommendation in Social Media
    • Image-based Point of Interest Recommendation
    • Image Generation and Recommendation
  • Video/Audio-based Recommendation
    • Video Recommendation
    • Music Recommendation
    • Speech Proceeding for Recommendation
  • Social Recommendation
    • Heterogenous Network Analysis for Recommendation
    • Friend Recommendation
    • Personalized News Feeding
    • Multimedia Recommendation in Social Networks
  • Multiple Information Fusion for Recommendation
    • Heterogenous Information Analysis for Recommendation
    • Structured and Unstructured Data Analysis for Recommendation
  • Explainable Recommendation
    • Text-based Explainable Recommendation
    • Image-based Explainable Recommendation
    • Knowledge-base for Explainable Recommendation
    • Social Explainable Recommendation
    • Explainable Recommendation in Novel Applications
  • User Modeling with Multimodal Information
    • User modeling with heterogenous data
    • User modeling based on social media
    • User modeling based on big data analytics
    • Preference inference based on explicit/implicit feedback
  • Exploiting Homogeneous/Heterogenous Information
    • Multi-Criteria Ratings based Recommender Systems
    • Hierarchical/Multi-Facet Data Modeling for Item Recommendation
    • Integrating both Explicit and Implicit Feedback for Recommendations
    • Cross-domain Feedback Exploitation for recommendations
    • Multi-view Machine Learning for Recommendation
    • Cross-Device Information Fusion for Recommendation
  • Addressing Special Issues in Recommender Systems
    • Resolving Cold-start and Data Sparsity with Auxiliary Information
    • Enhancing Recommendation Novelty and Explainability
    • Scalability when Integrating Multiple kinds of Auxiliary Information
    • Toolkits to Improve the Reproducibility of Recommendation Models
  • New Dataset and Applications
    • New Dataset Papers for Recommender Systems
    • New Recommendation Applications in Practice

We also welcome papers that are broadly related to the topics even if they are not explicitly listed above.

Important Dates
  • Submission Deadline: Nov 20, 2017
  • Notification: Dec 15, 2017
  • Workshop Day: Feb 9, 2018
Submission

All workshop submissions must be formatted according to ACM SIG Proceedings template, and we welcome submissions either long or short format:

  • Long paper: max 8 pages
  • Short paper: max 4 pages
  • Demo paper: 2-4 pages

Authors should submit original papers in PDF format through the Easychair system at: https://easychair.org/conferences/?conf=ifup2018

Committees

Workshop Chairs

  • Feida Zhu, Singapore Management University, Singapore
  • Yongfeng Zhang, University of Massachusetts Amherst, USA
  • Neil Yorke-Smith, Delft University of Technology, and American University of Beirut
  • Guibing Guo, Northeastern University, China
  • Xu Chen, Tsinghua University, China

Programme Committee

  • Paolo Cremonesi, Politecnico di Milano
  • Xiangnan He, National University of Singapore
  • Bin Li, NICTA, Australia
  • Xin Liu, Institute for Infocomm Research, Singapore
  • Weike Pan, Shenzhen University, China
  • Alan Said, CWI
  • Yue Shi, Yahoo
  • Yao Wu, Twitter
  • Fuzheng Zhang, Microsoft Research Asia
  • Yong Zheng, DePaul University, USA
Aug 18, 2017
We are accepting submissions, please consider to submit your research papers and share with the community!
Aug 16, 2017
The workshop website is online!
Aug 12, 2017
The IFUP 2018 Workshop will be co-located with WSDM 2018. Look forward to seeing you!