IFUP 2016 - 2016 Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization

Recently, auxiliary information (e.g., social friends, item content) has been incorporated in many recommendation models to enhance the performance of both rating prediction and item ranking. However, the used auxiliary data is often referred to as single-dimensional information, such as social trust or item category. Many existing studies focus on how to make the best use of a single facet, such as temporal factors or geo-locations to improve recommendations. However, with the advent of context-aware recommender systems, it gets more and more important to incorporate multiple kinds of auxiliary information the case of which is closer to and more prevalent in practice. The information may be either homogenous or heterogeneous. For example, it may be necessary to consider multiple social relationships (e.g., social trust, friendship, membership, followship) simultaneously to make recommendations rather than merely one of them. Another example is that product recommendation may take into account all kinds of users’ historical data, including purchase, click, browse and wanted list. On one hand, information in different dimensions reflects various views of user modeling and preferences. On the other hand, information from different dimensions is often co-related and dependent in some manner. In this regard, it is necessary to consider all these kinds of information as a whole for user modeling and for further improving recommendation performance. Therefore, how to effectively leverage multi-dimensional information and how these dimensions interacting with each other influence recommendations are the two challenging questions the research community need to resolve.
The international workshop IFUP 2016 aims to provide a dedicated forum for discussing open problems, challenges and innovative research approaches in fusing multi-dimensional information for user modeling 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 recommendations.
The scope of the workshop includes (but is not limited to):
User modeling
User modeling based on social media
User modeling based on big data analytics
Preference inference based on implicit feedback
General recommendation problems
Social recommender systems
Content-based recommender systems
Location or POI-aware recommender systems
Context-aware recommender systems ?
Exploiting homogeneous/heterogenous information
Multi-criteria ratings based recommender systems
Multi-type social relationships comparison and fusion for recommendations
Multi-level and hierarchical item relationships for item recommendations
Multi-type implicit feedback fusion for recommender systems
Integrating both explicit and implicit feedback for recommendations
Cross-domain feedback and knowledge exploitation for recommendations
Multi-view learning and cross-device information fusion
Online and offline recommendation
Personalization for online and offline search social interaction
Online and offline recommendation for product purchase, information acquisition and establishment of social relations ?
Addressing issues of recommender systems
Resolving the 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
Committees
Workshop Chairs
Robin Burke, DePaul University, US
Feida Zhu, Singapore Management University, Singapore
Neil Yorke-Smith, American University of Beirut, Lebanon
Guibing Guo, Northeastern University, China
Programme Committee
Bin Li, NICTA, Australia
Xin Liu, Institute for Infocomm Research, Singapore
Weike Pan, Shenzhen University, China
Alan Said, CWI
Yue Shi, Yahoo
Zhu Sun, Nanyang Technological University, Singapore
Domonkos Tikk, Gravity R&D
Yong Zheng, DePaul University, US