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Recommendation Solutions

Context-aware recommender systems (CARS) can be built to adapt the list of item recommendations to different context situations (e.g., time, location, companion, budget, weather, etc.). Users can receive the appropriate recommendations tailored by their preferences in specific contexts.

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The CARSKit and DeepCARSKit libraries were developed and released by Dr. Yong Zheng's research team. Dr. Yong Zheng is currently a tenure-track Assistant Professor at Department of Information Technology and Management, College of Computing, Illinois Institute of Technology, Chicago, USA. His research interests lie in Data Science, Personalizations, Recommender Systems, Context-Awareness, Human-Centric Computing, Technology-Enhanced Learning, and so forth.
      His research were published in reputational conferences and journals, such as KDD, ICDM, CIKM, RecSys, UMAP, IUI, SAC, UMUAI, Neurocomputing, etc. He is active in professional services. He was invited to serve on the organization committee in serveral ACM conferences, such as ACM RecSys, ACM UMAP, ACM IUI, ACM Hypertext. He is a PC member and reviewer for several well-known academic conferences and journals, such as IJCAI, KDD, SIGIR, WWW, RecSys, UMAP, TOIS, TIST, TiiS, TKDE, UMUAI, etc.

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