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MRTensorCube: tensor factorization with data reduction for context-aware recommendations


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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  1. Sangkeun L, Juno C, Sang-goo L (2011) Survey and trend analysis of context-aware systems. Information 14:527–548

    Google Scholar 

  2. Tejeda-L Alvaro, Carlos P, Eduardo P, Rosa S, Herrera-V Enrique (2014) A quality based recommender system to disseminate information in a university digital library. Inf Sci 261:52–69. doi:10.1016/j.ins.2013.10.036

    Article  Google Scholar 

  3. Bill S, Norman A, Roy W (1994) Context-aware computing applications. Workshop Mob Comput Syst Appl 1:85–90. doi:10.1109/MCSA.1994.512740

    Article  Google Scholar 

  4. Wookey L, Carson K, James J (2011) Mobile web navigation in digital ecosystems using rooted directed trees. IEEE Trans Ind Electron 58:2154–2162. doi:10.1109/TIE.2010.2050292

    Article  Google Scholar 

  5. Yehuda K, Robert B, Chris V (2009) Matrix factorization techniques for recommender systems. Computer 42:30–37. doi:10.1109/MC.2009.263

    Article  Google Scholar 

  6. Gediminas A, Ramesh S, Shahana S, Alexander T (2005) Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans Inf Syst 23:103–145. doi:10.1145/1055709.1055714

    Article  Google Scholar 

  7. Alexandros K, Xavier A, Linas B, Nuria O (2010) Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: ACM Conference on RecSys’10, pp 79–86. doi:10.1145/1864708.1864727

  8. Steffen R, Zeno G, Christoph F, Lars ST (2011) Fast context-aware recommendations with factorization machines. ACM SIGIR Conference on Research and Development in Information Retrieval, pp 635–644: doi:10.1145/2009916.2010002

  9. Steffen R, Leandro BM, Alexandros N, Lars ST (2009) Learning optimal ranking with tensor factorization for tag recommendation. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09, pp 727–736. doi:10.1145/1557019.1557100

  10. Suan L, Jinho K (2015) Efficient level-based top-down data cube computation using mapreduce. Lect Notes Comput SC 9260:1–19. doi:10.1007/978-3-662-47804-2_1

    Article  Google Scholar 

  11. Suan L, Jinho K (2016) Technology for electronic document management and virtual storage system in cloud environments. J IT Archit 13:179–190

    Google Scholar 

  12. Suan L, Sunhwa J, Jinho K (2015) MRDataCube: Data cube computation using map reduce. In: BigComp2015, pp 95–102. doi:10.1109/35021BIGCOMP.2015.7072817

  13. Jim G, Surajit C, Adam B et al (1997) Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Min Knowl Discov 1:29–53. doi:10.1023/A:1009726021843

    Article  Google Scholar 

  14. Sameet A, Rakesh A, Prasad D et al (1996) On the computation of multidimensional aggregates. In: Proceedings International Conference on Very Large Data Bases. pp 506–521

  15. Frank D, Todd E, Andrew RC (2006) The cgmCUBE project: optimizing parallel data cube generation for ROLAP. Distrib Parallel Database 19:29–62. doi:10.1007/s10619-006-6575-6

    Article  Google Scholar 

  16. Suan L, Jinho K (2011) Efficient Distributed parallel top-down computation of data cube using MapReduce. Lect Notes Comput SC 7448:168–179. doi:10.1007/978-3-642-32584-7_14

    Article  Google Scholar 

  17. Zipf’s law.’s_law

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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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Correspondence to Yong-Ik Yoon.

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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).

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  • Context awareness
  • Tensor data cube
  • MapReduce framework