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A new approach for rating prediction system using collaborative filtering

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Abstract

Recommendation systems are most commonly used to recommend items for web users. It assists users in the selection of product from millions of product. E-Commerce websites such as AMAZON recommend items to its customers. The recommendation system mainly depends upon the previous history of its users. In this paper, a new User Rating Prediction (URP) algorithm is proposed to predict ratings for items. The proposed URP algorithm mainly depends upon similarity of users and assumes that users with similar taste may be interested in similar items. The proposed system first makes a list of related users for every user and then uses this information to predict ratings for different items. The result of the proposed algorithm was compared with the previous existing methods. The proposed algorithm gives small value of Mean Absolute Error (MAE) and root-mean-square error (RMSE) as compared to other methods.

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References

  1. Chai, T., Draxler, R.R.: Root mean square error (rmse) or mean absolute error (mae)? Geosci. Model Dev. Discuss. 7, 1525–1534 (2014)

    Article  Google Scholar 

  2. Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)

    Article  Google Scholar 

  3. Devika, P., Jisha, R., Sajeev, G.: A novel approach for book recommendation systems. In: Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference on, pp. 1–6. IEEE (2016)

  4. Gong, S.: A collaborative filtering recommendation algorithm based on user clustering and item clustering. JSW 5(7), 745–752 (2010)

    Article  Google Scholar 

  5. GroupLens. Movielens 100k dataset. http://grouplens.org/datasets/movielens/100k/, May 2018 Accessed on 2018-04-03

  6. Gupta, J., Gadge, J.: A framework for a recommendation system based on collaborative filtering and demographics. In: Circuits, Systems, Communication and Information Technology Applications (CSCITA), 2014 International Conference on, pp. 300–304. IEEE (2014)

  7. Gupta, J., Gadge, J.: Performance analysis of recommendation system based on collaborative filtering and demographics. In: Communication, Information and Computing Technology (ICCICT), 2015 International Conference on, pp. 1–6. IEEE (2015)

  8. Isinkaye, F., Folajimi, Y., Ojokoh, B.: Recommendation systems: principles, methods and evaluation. Egypt. Inf. J. 16(3), 261–273 (2015)

    Article  Google Scholar 

  9. Kumar, P., Thakur, R.S.: Recommendation system techniques and related issues: a survey. Int J Inf Technol 10, 1–7 (2018)

    Google Scholar 

  10. Kumar, P., Thakur, R.S.: A framework for weblog data analysis using hive in hadoop framework. In: Proceedings of International Conference on Recent Advancement on Computer and Communication, pp. 433–439. Springer (2018)

  11. Kumar, V., Kumar, P., Thakur, R.: A brief investigation on data security tools and techniques for big data. Int. J. Eng. Sci. Invention 6(9), 20–27 (2017)

    Google Scholar 

  12. Li, X., Murata, T.: Using multidimensional clustering based collaborative filtering approach improving recommendation diversity. In: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 03, pp. 169–174. IEEE Computer Society (2012)

  13. Malviya, B.K., Agrawal, J.: A study on web usage mining theory and applications. In: Communication Systems and Network Technologies (CSNT), 2015 Fifth International Conference on, pp. 935–939. IEEE (2015)

  14. Moghaddam, S.G., Selamat, A.: A scalable collaborative recommender algorithm based on user density-based clustering. In: Data Mining and Intelligent Information Technology Applications (ICMiA), 2011 3rd International Conference on, pp. 246–249. IEEE, (2011)

  15. Rathod, A., Indiramma, M.: A survey of personalized recommendation system with user interest in social network. Int. J. Comput. Sci. Inf. Technol. 6(1), 413–415 (2015)

    Google Scholar 

  16. Rodrigues, C.M., Rathi, S., Patil, G.: An efficient system using item & user-based cf techniques to improve recommendation. In: Next Generation Computing Technologies (NGCT), 2016 2nd International Conference on, pp. 569–574. IEEE (2016)

  17. Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Recommender systems for large-scale e-commerce: scalable neighborhood formation using clustering. Proc. Fifth Int. Conf. Comput. Inf. Technol. 1, 291–324 (2002)

    Google Scholar 

  18. Valera, M., Rathod, K.: A novel approach of mining frequent sequential pattern from customized web log preprocessing. Int. J. Eng. Res. Appl. 3(1), 269–380 (2013)

    Google Scholar 

  19. Wang, M.-J., Han, J.-T.: Collaborative filtering recommendation based on item rating and characteristic information prediction. In: Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on, pp. 214–217. IEEE (2012)

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Authors and Affiliations

  1. Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, 462003, India

    Pushpendra Kumar, Vinod Kumar & Ramjeevan Singh Thakur

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  1. Pushpendra Kumar
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  2. Vinod Kumar
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  3. Ramjeevan Singh Thakur
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Correspondence to Pushpendra Kumar.

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Kumar, P., Kumar, V. & Thakur, R.S. A new approach for rating prediction system using collaborative filtering. Iran J Comput Sci 2, 81–87 (2019). https://doi.org/10.1007/s42044-018-00028-5

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  • DOI: https://doi.org/10.1007/s42044-018-00028-5

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