Advertisement

Intelligent and Integrated Book Recommendation and Best Price Identifier System Using Machine Learning

  • Akanksha Goel
  • Divanshu Khandelwal
  • Jayant Mundhra
  • Ritu Tiwari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

When one wants to read something, it is tough to decide the just perfect one book that one would want to read. And, given such a system is conceived and the reader can find the book he/she wishes to read, how does he/she decide where to buy that book from? Again, there are a plethora of vendors selling the same book at different prices. Thus, this paper proposes a dynamic recommendation system by extracting the relevant data from different E-portals and applying Hybrid Filtering Approach (collaborative and content-based filtering) on the collected data to recommend the books. This system makes use of user-based collaborative filtering approach using cosine similarity rule and is optimized with bee algorithm, and the results are refined by applying natural language processing on the reviews. The paper also intends to solve cold start problem by extracting available user preferences from Facebook API.

Keywords

Data analysis Nature-inspired algorithms Natural language processing Data scrapping Recommendation system 

References

  1. 1.
    Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods evaluation. Egypt. Inf. J. 16, 261–273 (2015)CrossRefGoogle Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 16, 1041–4347 (2005)Google Scholar
  3. 3.
    Kanetkar, S., Nayak, A., Swamy, S., Bhatia, G.: Web-based personalized hybrid book recommendation system. In: IEEE International Conference on Advances in Engineering & Technology Research, Unnao, India (2014).  https://doi.org/10.1109/icaetr.2014.701292
  4. 4.
    Chandak, M., Girase, S., Mukhopadhyay, D.: Introducing hybrid technique for optimization of book recommender system. Proc. Comput. Sci. 45: 23–31 (2015). ElsevierCrossRefGoogle Scholar
  5. 5.
    Kwon, K., Park, Y.: Collaborative filtering using dual information sources. IEEE Intell. Syst. 22:1541–1672 (2007).  https://doi.org/10.1109/mis.2007.48
  6. 6.
    Tewari, A.S., Kumar, A., Barman, A.G.: Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining. In: Advance Computing Conference (IACC), IEEE International, Gurgaon, India (2014).  https://doi.org/10.1109/iadcc.2014.6779375
  7. 7.
    Anne, H.H.Ngu, Segev, A., Jian, Yu., Sheng, Q.Z., Yao, L.: Unified collaborative and content based web service recommendation. IEEE Trans. Serv. Comput. 8, 453–466 (2014)Google Scholar
  8. 8.
    Yang, X-S.: Nature-Inspired Optimization Algorithms Book. Elsevier (2014)CrossRefGoogle Scholar
  9. 9.
    Pham, D.T., Castellani, M., Le Thi, H.A.: Nature-inspired intelligent optimisation using the Bees algorithm. In: Transactions on Computational Intelligence XIII, vol. 8342, pp. 38–69. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  10. 10.
    Lei, X., Qian, X., Zhao, G.: Rating prediction based on social sentiment from textual reviews. IEEE Trans. Multimed. 18, 1910–1921 (2016).  https://doi.org/10.1109/TMM.2016.2575738CrossRefGoogle Scholar
  11. 11.
    Mahajan, C., Mulay, P.: E3: effective emoticon extractor for behavior analysis from social media. Proc. Comput. Sci. 50, 610–616 (2014). ElsevierCrossRefGoogle Scholar
  12. 12.
    Hirschberg, J., Ballard, B.W., Hindle, D.: Natural language processing. AT&T Tech. J. IEEE 67, 41–57 (1988).  https://doi.org/10.1002/j.1538-7305.1988.tb00232.xCrossRefGoogle Scholar
  13. 13.
    Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 5–53 (2004)CrossRefGoogle Scholar
  14. 14.
    Beel, J., Gipp, B., Langer, S., Breitinger, C.: Research-paper recommender systems: a literature survey. Int. J. Digit. Libr. 17, 305–338 (2015)CrossRefGoogle Scholar
  15. 15.
    Kim, H., Ha, I., Lee, K., Jo, G., El-Saddik, A.: Collaborative user modeling for enhanced content filtering in recommender systems. Decis. Support Syst. 51, 772–781 (2011)CrossRefGoogle Scholar
  16. 16.
    Smith, B., Linden, G.: Two decades of recommender systems at Amazon.com. IEEE Internet Comput. 21, 12–18 (2017)CrossRefGoogle Scholar
  17. 17.
    Xu, C.: Personal recommendation using a novel collaborative filtering algorithm in customer relationship management. Discrete Dynamics in Nature and Society, Hindawi (2013). http://dx.doi.org/10.1155/2013/739460
  18. 18.
    López-Nores, M., Blanco-Fernández, Y., Pazos-Arias, J., Gil-Solla, A.: Property-based collaborative filtering for health-aware recommender systems. Expert Syst. Appl. 39, 7451–7457 (2012)CrossRefGoogle Scholar
  19. 19.
    Altingovde, I., Subakan, Ö., Ulusoy, Ö.: Cluster searching strategies for collaborative recommendation systems. Inf. Process. Manage. 49, 688–697 (2013)CrossRefGoogle Scholar
  20. 20.
    Göksedef, M., Gündüz-Öğüdücü, Ş.: Combination of web page recommender systems. Expert Syst. Appl. 37, 2911–2922 (2010)CrossRefGoogle Scholar
  21. 21.
    Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40, 66–72 (1997)CrossRefGoogle Scholar
  22. 22.
    Pessemier, T., Dhondt, J., Martens, L.: Hybrid group recommendations for a travel service. Multimed. Tools Appl. 76, 2787–2811 (2016)CrossRefGoogle Scholar
  23. 23.
    Kumar, N., Fan, Z.: Hybrid user-item based collaborative filtering. Proc. Comput. Sci. 60, 1453–1461 (2015)CrossRefGoogle Scholar
  24. 24.
    Hwang, C.S., Chen, Y.P.: Using trust in collaborative filtering recommendation. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Kyoto, Japan, vol. 4570, pp. 1052–1060. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-73325-6_105
  25. 25.
    Tarus, J.K., Niu, Z., Yousif, A.: A hybrid knowledge-based recommender system for e learning based on ontology and sequential pattern mining. Future Generation Computer Systems, 72:37–48 (2017). ElsevierCrossRefGoogle Scholar
  26. 26.
  27. 27.
  28. 28.
  29. 29.
  30. 30.
  31. 31.
  32. 32.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Akanksha Goel
    • 1
  • Divanshu Khandelwal
    • 1
  • Jayant Mundhra
    • 1
  • Ritu Tiwari
    • 1
  1. 1.Robotics and Intelligent System Design LabABV-IIITM GwaliorGwaliorIndia

Personalised recommendations