Skip to main content
Springer Nature Link
Log in

Adaptable and proficient Hellinger Coefficient Based Collaborative Filtering for recommendation system

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Currently in many real time applications, recommender systems play a prominent role in helping the customers by selecting the most suitable products of their requirements. Generally the scheme of recommending the products is observed as single individual task. To enhance this task, the collaborating filtering is used as more effective charge in many situations. One of the major issues of the recommender system is difficult in learning the behavior of the group of the products and nature of the customers. Also in this internet era, the data collected day by day keeps on increasing and leads to information overloaded issue .Under this situation it is recommended to propose a novel collaborative filtering based methodology named ‘Hellinger Coefficient Based Collaborative Filtering (HCBCF)’ to search, find and rate the recommending products. The efficiency of the proposed method is evaluated on movie lens and Netflix recommendation datasets, and also this proves to be superior among other existing recommendation algorithms on comparing its efficiency metrics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Bobadilla, J., Ortega, F., Hernando, A.: A collaborative filtering similarity measure based on singularities. Inf. Process. Manag. 48(1), 204–217 (2012)

    Article  Google Scholar 

  2. Herlocker, J., Konstan, J.A., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 5(1), 287–310 (2002)

    Article  Google Scholar 

  3. Wang, Y.-F., Chuang, Y.-L., Hsu, M.-H., Keh, H.-C.: A personalized recommender system for the cosmetic business. Expert Syst. Appl. 1(1), 427–434 (2004)

    Article  Google Scholar 

  4. Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer, Berlin (2011)

  5. Jin, R., Si, L., Zhai, C.: A study of mixture models for collaborative filtering. Inf. Retr. 9(1), 357–382 (2006)

    Article  Google Scholar 

  6. Gong, S.: Employing user attribute and item attribute to enhance the collaborative filtering recommendation. J. Softw. 4(1), 883–890 (2009)

    Google Scholar 

  7. Bellogin, A., Cantador, I., Castells, P.: A comparative study of heterogeneous item recommendations in social systems. Inf. Sci. 221, 142–169 (2013)

    Article  MathSciNet  Google Scholar 

  8. Suresh, A., Varatharajan, R.: Competent resource provisioning and distribution techniques for cloud computing environment. Clust. Comput. J. Netw. Softw. Tools Appl. ISSN: 1386-7857 (Print) 1573- 7543 (2017) (Online). https://doi.org/10.1007/s10586-017-1293-6

  9. Rosli, A.N., You, T., Ha, I., Chung, K.-Y., Jo, G.-S.: Alleviating the cold-start problem by incorporating movies facebook pages. Clust. Comput. 18(1), 187–197 (2015)

    Article  Google Scholar 

  10. Xiaojun, L.: An improved clustering-based collaborative filtering recommendation algorithm. Clust. Comput. 20(2), 1281–1288 (2017)

    Article  Google Scholar 

  11. Blanco-Fernandez, Y., Lopez-Nores, M., Gil-Solla, A., Ramos-Cabrer, M., Pazos-Arias, J.: Exploring synergies between content-based filtering and spreading activation techniques in knowledge-based recommender systems. Inf. Sci. 181, 4823–4846 (2011)

    Article  Google Scholar 

  12. Kim, B.M., Li, Q., Park, C.S., Kim, S.G., Kim, J.Y.: A new approach for combining content-based and collaborative filters. J. Intell. Syst. 27(1), 79–91 (2006)

    Article  Google Scholar 

  13. Gong, S.: An efficient collaborative recommendation algorithm based on item clustering. In: Luo, Q. (ed.) Advances in Wireless Networks and Information Systems. Lecture Notes in Electrical Engineering, vol. 72, pp. 381–387. Springer, Berlin, Heidelberg (2010)

    Chapter  Google Scholar 

  14. Deng, W., Patil, R., Najjar, L., Shi, Y., Chen, Z.: Incorporating community detection and clustering techniques into collaborative filtering model. Procedia Comput. Sci. 31(1), 66–74 (2014)

    Article  Google Scholar 

  15. Yang, C., Yu, X., Liu, Y., Nie, Y., Wang, Y.: Collaborative filtering with weighted opinion aspects. Neurocomputing 210(1), 185–196 (2016)

    Article  Google Scholar 

  16. Ghazarian, S., Nematbakhsh, M.A.: Enhancing memory-based collaborative filtering for group recommender systems. Expert Syst. Appl. 42(7), 3801–3812 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Lekakos, G., Caravelas, P.: A hybrid approach for movie recommendation. Multimed. Tools Appl. 36(1), 55–70 (2008)

    Article  Google Scholar 

  19. Lekakos, G., Giaglis, G.M.: A hybrid approach for improving predictive accuracy of collaborative filtering algorithms. User Model User-Adap Interaction 17(1), 5–40 (2007)

    Article  Google Scholar 

  20. Salehi, M., Pourzaferani, M., Razavi, S.A.: Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model. Egypt. Inform. J. 14(1), 67–78 (2013)

    Article  Google Scholar 

  21. Zapata, A., Menendez, V.H., Prieto, M.E., Romero, C.: Evaluation and selection of group recommendation strategies for collaborative searching of learning objects. Int. J. Hum. Comput. Stud. 76, 22–39 (2015)

    Article  Google Scholar 

  22. Bobadilla, J., Serradilla, F., Hernando, A.: MovieLens, collaborative filtering adapted to recommender systems of e-learning. Knowl. Based Syst. 22(4), 261–265 (2009)

    Article  Google Scholar 

  23. Liu, D.R., Omar, H., Liou, C.H., Chi, H.C., Hsu, C.H.: Recommending blog articles based on popular event trend analysis. Inf. Sci. 305(1), 302–319 (2015)

    Article  Google Scholar 

  24. Li, Y., Lu, L., Xuefeng, L.: A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-commerce. Expert Syst. Appl. 28(1), 67–77 (2005)

    Article  Google Scholar 

  25. Konstan, J.A.: Introduction to recommender systems: algorithms and evaluation. ACM Trans. Inf. Syst. 22(1), 1–4 (2004)

    Article  Google Scholar 

  26. Li, Q., Khosla, R.: An adaptive algorithm for improving recommendation quality of e-recommendation systems. In: International Symposium on Computational Intelligence for Measurement Systems and Applications, Switzerland, July 29–31, pp. 199–203 (2003)

  27. Lops, P., Gemmis, M., Semeraro, G.: Recommender Systems Handbook. Springer, Berlin (2011)

    Google Scholar 

  28. Ye, H.: A personalized collaborative filtering recommendation using association rules mining and self-organizing map. J. Softw. 6(4), 732–739 (2011)

    Article  Google Scholar 

  29. Jamalzehi, S., Menhaj, M.B.: A new similarity measure based on item proximity and closeness for collaborative filtering recommendation. In: 4th International Conference on Control, Instrumentation, and Automation (ICCIA) 27–28, Qazvin Islamic Azad University, Qazvin, Iran (2016)

  30. Kardan, A., Ebrahimi, M.: A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups. Inf. Sci. 219(1), 93–110 (2013)

    Article  Google Scholar 

  31. Zhang, T., Iyengar, V.S.: Recommender systems using linear classifiers. J. Mach. Learn. Res. 2(1), 313–334 (2002)

    MATH  Google Scholar 

  32. Agrawal, P., Kadiya, T., Prajapati, R.: An efficient collaborative filtering using new user similarity measure for recommendation. IJSRD 5(2) (2017)

  33. Saranya, K.G., Sadasivam, G.S.: Modified heuristic similarity measure for personalization using collaborative filtering technique. Appl. Math. Inf. Sci. 11(1), 307–315 (2017)

    Article  Google Scholar 

  34. Liu, H., Zheng, H., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowl. Based Syst. 56(1), 156–166 (2014)

    Article  Google Scholar 

  35. Arsan, T., Koksal, E., Bozkuş, Z.: Comparison Of collaborative filtering algorithms with various similarity measures for movie recommendation. IJCSEA 6(3) (2016)

  36. Duwairi, R., Ammari, H.: An enhanced CBAR algorithm for improving recommendation systems accuracy. Simul. Modell. Pract. Theory 60(1), 54–68 (2016)

    Article  Google Scholar 

  37. Salehi, M.: Application of implicit and explicit attribute based collaborative filtering and BIDE for learning resource recommendation. Data Knowl. Eng. 87, 130–145 (2013)

    Article  Google Scholar 

  38. Pan, W., Liu, Z., Ming, Z., Zhong, H., Wang, X., Congfu, X.: Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation. Knowl. Based Syst. 85(1), 234–244 (2015)

    Article  Google Scholar 

  39. Zhang, F., Gong, T., Lee, V.E., Zhao, G., Rong, C., Qu, G.: Fast algorithm to evaluate collaborative filtering recommender systems. Knowl. Based Syst. 96(1), 96–103 (2016)

    Google Scholar 

  40. Kanta, V., Bharadwaj, K.K.: Enhancing recommendation quality of content-based filtering through collaborative predictions and fuzzy similarity measures. Procedia Eng. 38, 939–944 (2012)

    Article  Google Scholar 

  41. Kothari, A.A., Patel, W.D.: A novel approach towards context based recommendations using support vector machine methodology. Procedia Comput. Sci. 57, 1171–1178 (2015)

    Article  Google Scholar 

  42. Dhawana, S., Singhb, K., Jyotic: High rating recent preferences based recommendation system. Procedia Comput. Sci. 70(1), 259–264 (2015)

  43. Aher, S.B., Lobo, L.: Applicability of data mining algorithms for recommendation system in e-learning. In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics ACM, pp. 1034–1040 (2012)

  44. Panigrahia, S., Lenkaa, R.K., Stitipragyana, A.: A hybrid distributed collaborative filtering recommender engine using apache spark. Procedia Comput. Sci. 83(1), 1000–1006 (2016)

    Article  Google Scholar 

  45. Ahn, H.J.: A new similarity measure for collaborative filtering to alleviate the new user cold starting problem. Inf. Sci. 178(1), 37–51 (2008)

    Article  Google Scholar 

  46. Kawanoa, A., Hondaa, K., Kasugaia, H., Notsua, A.: A greedy algorithm for \(k\)-member co-clustering and its applicability to collaborative filtering. Procedia Comput. Sci. 22, 477–484 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  48. Li, X., Wang, Z.: A new recommendation algorithm combined with spectral clustering and transfer learning. Clust. Comput. (2017) https://doi.org/10.1007/s10586-017-1161-4

  49. Movielens data. http://www.grouplens.org/

  50. Neflix prize. http://www.netflixprize.com/

Download references

Author information

Authors and Affiliations

  1. Syed Ammal Engineering College, Ramanathapuram, Tamil Nadu, India

    M. Maheswari

  2. School of Computing, VIT, Chennai Campus, Chennai, Tamil Nadu, India

    S. Geetha

  3. Department of Computer Science and Engineering, GKM College of Engineering & Technology, Chennai, Tamil Nadu, India

    S. Selva kumar

Authors
  1. M. Maheswari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Geetha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Selva kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Geetha.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maheswari, M., Geetha, S. & Selva kumar, S. Adaptable and proficient Hellinger Coefficient Based Collaborative Filtering for recommendation system. Cluster Comput 22 (Suppl 5), 12325–12338 (2019). https://doi.org/10.1007/s10586-017-1616-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1616-7

Keywords

Search

Navigation