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Analysis of similarity measures in user-item subgroup based collaborative filtering via genetic algorithm

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Abstract

Recommender systems based on collaborative filtering (CF) analyze the mutual interests of similar users to predict the ratings of items for the active user (the user whom the prediction is for). This is done in two ways: (1) finding similarity between all users who share the same rating patterns with the active user (2) finding similarity between all pairs of items of different users by building an item-item matrix and thereafter inferring the tastes of the active user. We believe that considering the entire set of items is irrelevant in the prediction process and propose a user-item subgroup based local least squares CF technique that considers only a subset of highly correlated items based on set of similar users. An evolutionary algorithm framework is used to discover the subset of highly correlated items and a local least squares method is used to impute the missing ratings by analyzing the highly correlated user-item subgroup. As far as the experimental setup is concerned, three different similarity measures are used in the implementation of our proposed algorithm and a comparison is made with the traditional local least squares approach as well as with a state-of-the-art CF algorithm. Benchmark datasets like MovieLens are used for the experimental results.

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References

  1. Bu J et al (2016) Improving collaborative recommendation via user-item subgroups. IEEE Trans Knowl Data Eng 28(9):2363–2375

    Article  Google Scholar 

  2. Xu et al (2012) An exploration of improving collaborative RS via user-item subgroups. In: Proceedings of the 21st international conference on World Wide Web, WWW’12, ACM, pp 21–30

  3. Hellem T et al (2004) LSimpute: accurate estimation of missing values in microarray data with LS methods. Nucleic Acids Res 32(3):e34+

    Article  Google Scholar 

  4. Kim H et al (2005) Missing value estimation for DNA microarray gene expression data: local least squares imputation. Bioinformatics 21(2):187–198

    Article  MathSciNet  Google Scholar 

  5. Cheng K-O et al (2012) Iterative bicluster-based least square framework for estimation of missing values in microarray gene expression data. Pattern Recognit 45(4):1281–1289

    Article  Google Scholar 

  6. Cai Z et al (2006) Iterated local least squares microarray missing value imputation. J Bioinform Comput Biol 4(5):935–958

    Article  Google Scholar 

  7. Divina F et al (2006) Biclustering of expression data with evolutionary computation. IEEE Trans Knowl Data Eng 18(5):590–602

    Article  Google Scholar 

  8. Ayangleima L et al (2016) Collaborative filtering, matrix factorization and population based search: the nexus unveiled. In: Neural information processing—23rd international conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, proceedings, Part III, pp 352–361

  9. Navgaran et al (2013) Evolutionary based mf method for collaborative filtering systems. In: Electrical engineering (ICEE), 2013 21st Iranian conference on, pp 1–5

  10. Sowmini Devi V et al (2014) Collaborative filtering by PSO-based MMMF. In: Systems, man and cybernetics (SMC), 2014 IEEE international conference on, IEEE, pp 569–574

  11. Abbas Assad et al (2015) A survey on context-aware recommender systems based on computational intelligence techniques. Computing 97(7):667–690

    Article  MathSciNet  Google Scholar 

  12. Sarwar et al (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, ACM, pp 285–295

  13. SV Chande, Sinha M (2009) Genetic algorithm: a versatile optimization tool. BIJIT-BVICAM’s Int J Inf Technol 1(1):7–12

    Google Scholar 

  14. Cheng et al (2000) Biclustering of expression data. In: Proceedings of the eighth international conference on intelligent systems for molecular biology, pp 93–103. AAAI Press

  15. Ayangleima L et al (2015) Bi-clustering of gene expression microarray using coarse grained parallel genetic algorithm (CGPGA) with migration. In: 2015 annual IEEE India conference (INDICON), pp 1–6

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Authors and Affiliations

  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500046, India

    Ayangleima Laishram, Vineet Padmanabhan & Rajendra Prasad Lal

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  1. Ayangleima Laishram
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  2. Vineet Padmanabhan
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  3. Rajendra Prasad Lal
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Correspondence to Ayangleima Laishram.

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Laishram, A., Padmanabhan, V. & Lal, R.P. Analysis of similarity measures in user-item subgroup based collaborative filtering via genetic algorithm. Int. j. inf. tecnol. 10, 523–527 (2018). https://doi.org/10.1007/s41870-018-0195-z

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  • DOI: https://doi.org/10.1007/s41870-018-0195-z

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