Workshop on Intelligent Recommender Systems by Knowledge Transfer and Learning

Growth of online information systems has led to an abundance of data that is heterogeneous, noisy, and changes rapidly. The data used by recommender systems, in forms of implicit or explicit user feedback, follow the same trend: the feedback can be in various formats and collected from multiple resources, the collected feedback is uncertain, and user taste and item popularities can change over time. Cross-domain recommender systems and transfer learning approaches propose to effectively take advantage of such heterogeneity in the data to provide better-quality recommendations and resolve issues such as the cold-start problem. This workshop intends to create a medium to generate more practical and efficient predictive models or recommendation approaches by leveraging user feedback or preferences from multiple domains, transferring useful information across multiple viewpoints to the data, and leveraging the context available in the data.

  • Shaghayegh, Sherry) Sahebi, University at Albany, SUNY, USA
  • Yong Zheng, Illinois Institute of Technology, USA
  • Weike Pan, Shenzhen University, China
  • Ignacio Fern├índez, NTENT, Spain


Saturday, Oct 6, 2018

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