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

Authors
Kim S.Yoon Y.-I.
Issue Date
Mar-2016
Publisher
IEEE
Keywords
4-way tensor; Context Awareness; PARAFAC; Recommendation system; Tensor Factorization
Citation
2016 International Conference on Big Data and Smart Computing (BigComp), v.2016-MARCH, pp 18 - 23
Pages
6
Journal Title
2016 International Conference on Big Data and Smart Computing (BigComp)
Volume
2016-MARCH
Start Page
18
End Page
23
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/9987
DOI
10.1109/BIGCOMP.2016.7425796
ISSN
2375-9356
Abstract
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. © 2016 IEEE.
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