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Context-Aware Collaborative Prediction

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Context-Aware Collaborative Prediction

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Abstract

Context-aware collaborative prediction takes contextual information into consideration when modeling user preferences and predicting user behaviors. There are two general ways to integrate contexts with collaborative prediction: contextual filtering and contextual modeling. Contextual filtering uses contexts to select data and adjust the result, while contextual modeling takes contexts into the model construction. Currently, the most effective context-aware collaborative prediction algorithms are based on the contextual modeling approach, which generates contextual representations or context-aware representations. This chapter reviews some related tasks of collaborative prediction, such as conventional recommendation, sequential recommendation, and multi-domain relation prediction. In addition, it also introduces some recent works on representation learning and methods of specific applications, such as context-aware recommendation, latent collaborative retrieval, tag recommendation, and click-through rate prediction.

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  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Shu Wu, Qiang Liu, Liang Wang & Tieniu Tan

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Wu, S., Liu, Q., Wang, L., Tan, T. (2017). Context-Aware Collaborative Prediction. In: Context-Aware Collaborative Prediction. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-10-5373-3_2

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