Abstract
Context information can be an important factor of user behavior modeling and various context recognition recommendations. However, state-of-the-art context modeling methods cannot deal with contexts of other dimensions such as those of users and items and cannot extract special semantics. On the other hand, some tasks for predicting multidimensional relationships can be used to recommend context recognition, but there is a problem with the generation recommendations based on a variety of context information. In this paper, we propose MRTensorCube, which is a large-scale data cube calculation based on distributed parallel computing using MapReduce computation framework and supports efficient context recognition. The basic idea of MRTensorCube is the reduction of continuous data combined partial filter and slice when calculating using a four-way algorithm. From the experimental results, it is clear that MRTensor is superior to all other algorithms.
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Acknowledgements
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (B0126-16-1041, Auto-Generated Media Service Technologies based on Semantic Relationship of Contents for Self-Growth Social Broadcasting).
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Kim, S., Lee, S., Kim, J. et al. MRTensorCube: tensor factorization with data reduction for context-aware recommendations. J Supercomput 76, 7847–7857 (2020). https://doi.org/10.1007/s11227-017-2002-1
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DOI: https://doi.org/10.1007/s11227-017-2002-1