Architecture of 4-way tensor factorization for context-aware recommendations

Contextual information has been recognized as an important factor to consider in user-aware Recommendation Systems. Since contextual information can be used as a significant factor in modeling user behavior, various context-aware recommendation methods are proposed. However, the state-of-the-art context modeling methods treat contexts as other dimensions similar to the dimensions of users and items, and cannot extract the special semantic operation of contexts. On the other hand, some works on multi-domain relation prediction can be used for the context-aware recommendation, but they have problems in generating recommendation under a large amount of contextual information. In this paper, we propose the 4-way Tensor, a parallel tensor factorization algorithm, to accelerate the tensor factorization of large datasets to support efficient context-aware recommendations. The basic idea of this algorithm is to partition a tensor into partition and then exploit the inherent parallelism to perform tensor related operations in parallel.