A Hybrid Recommendation System Based on Collaborative Filtering and Association Rule

Authors: Khushboo Sahu, Mayank Bhatt

Published in: IJRRETAS, Volume 3, Issue 6, pp. 1-5 (01 Jun 2017)

Impact Factor: 3.5 (2017)

License: CC BY 4.0 CC BY 4.0 (Open Access)

Certificate: View Certificate

Abstract

A recommendation engine is a feature (not a product) that filters items by predicting how a user might rate them. It solves the problem of connecting your existing users with the right items in your massive inventory (i.e. tens of thousands to millions) of products or content. Product recommendations are a must-have feature for all ecommerce websites, as they can drive sales, increase conversion rate and order value. Sending personalized product recommendations to your customers increases sales in just a few clicks. Add a Product Recommendations content block or merge tag to E-commerce Automation workflows to encourage subscribers to visit your store, re-engage inactive customers by promoting relevant items, suggest your best-selling products to new customers, and much more. Most of web portals have integrated this feature but web personalization is still a demanding issue. This project works target to integrate web personalization feature with product recommendation to enhance the sales and performance of E-commerce portal. A hybrid recommendation algorithm based on collaborative filtering and association rule will be used to recommend the products. Java technology will be used to develop these solutions.

Keywords
  • Product Recommendation
  • Collaborative Filtering
References
  1. Schafer, J. Ben, Joseph Konstan, and John
  2. Riedl. 1999. “Recommender Systems In E-
  3. Commerce.” In 1St ACM Conference On Electronic
  4. Commerce, 158-166.
  5. Su, Xiaoyuan, and Taghi M. Khoshgoftaar.
  6. 2009. “A Survey Of Collaborative Filtering
  7. Techniques.” Advances In Artificial Intelligence
  8. 2009: 1-19. doi:10.1155/2009/421425.
  9. Melville, Prem, and Vikas Sindhwani. 2010.
  10. “Recommender Systems.” Encyclopedia Of
  11. Machine Learning.
  12. Xingyuan Li.2011 “Collaborative Filtering
  13. Recommendation Algorithm Based on Cluster”
  14. International Conference on Computer Science and
  15. network Technology(ICCSNT), IEEE, 4: 2682-2685.
  16. Francesco Ricc, Lior Rokach, Bracha Shapira.2011. Recommender Systems Handbook. NY:Springer, 1-35
  17. Sarwar, George Kaypi, Joseph Konstan and John Riedl.2000. "Application of Dimensionality Reduction in Recommender Systems -- A 6 Study." In ACM WebKDD Workshop.
  18. Sarwar, George Kaypi, Joseph Konstan, John Riedl.2000. “Analysis of recommendation algorithms for E-commerce.” In the Second ACMConference on Electronic Commerce, 158–167 42
  19. Sarwar, George Kaypi, Joseph Konstan, John Riedl.2001. “Item-based Collaborative Filtering Recommendation Algorithms.” In the 10th International World Wide Web Conference, 285-295
  20. Gong, Songjie. 2010. “A Collaborative Filtering Recommendation Algorithm Based On User Clustering And Item Clustering.” JSW 5 (7). doi:10.4304/jsw.5.7.745-752.
  21. Yan Shi, Xiao, HongWu Ye, and SongJie Gong. 2008. “A Personalized Recommender Integrating Item-Based And User-Based Collaborative Filtering.” ISBIM '08 International Seminar On Business And Information Management 1 (2008): 264-267.
  22. Mangalindan, JP. 2012. “Amazon’S Recommendation Secret.” Fortune. http://fortune.com/2012/07/30/amazons-recommendation-secret/.
  23. Koren, Yehuda.2009. “Collaborative Filtering with Temporal Dynamics.” 15th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD 09), ACM (2009): 447-455.
  24. Liu, Jiahui, Dolan, Peter, Pedersen, Elin Rønby.2010. “ Personalized news recommendation based on click behavior”, In: Rich, et al. (eds.) In the 14th Int. Conf. on Intelligent User Interfaces (IUI), ACM, (2010): 31–40.
  25. Owen, Sean, Anil, Robin, Dunning, Ted, Friedman, Ellen. 2011 . Mahout in action. Shelter Island NY: Manning. () 43
  26. Walunj, Sachin, Sadafale, Kishor. 2013. “An online recommendation system for e-commerce based on apache mahout framework.” Proceedings of the 2013 annual conference on Computers and people research, ACM (2013): 153– 158.
  27. Verbert, Katrien, Drachsler, Hendrik,
  28. Manouselis, Nikos, Wolpers, Martin, Vuorikari,
  29. Vuorikari, Riina, Duval, Erik.2011. “Dataset-driven
  30. Research for Improving Recommender Systems for
  31. Learning.” 1st International Conference Learning
  32. Analytics & Knowledge, ACM (2011): 44-53. [17] Wikipedia,. 2015. “Cosine Similarity.”
  33. https://en.wikipedia.org/wiki/Cosine_similarity.
  34. Duryee, Tricia. 2014. “Amazon Adds 30
  35. Million Customers In The Past Year - Geekwire.”
  36. Geekwire. http://www.geekwire.com/2014/amazon-
  37. adds-30-millioncustomers-past-year/. [19] Grey,
  38. Paul. 2013. “How Many Products Does Amazon
  39. Sell? | Exportx.” Exportx. https://export-
  40. x.com/2013/12/15/many-products-amazon-sell/.
  41. Resnick, Paul, Iacovou, Neophytos, Suchak,
  42. Mitesh, Bergstrom, Peter, Riedl, John.1994. “
  43. GroupLens: an open architecture for collaborative
  44. filtering of netnews.” CSCW conference, ACM
  45. GroupLens,. 2013. “Movielens'.
  46. http://grouplens.org/datasets/movielens/.
How to Cite
APA: Sahu, K., & Bhatt, M. (2017). A Hybrid Recommendation System Based on Collaborative Filtering and Association Rule. International Journal for Rapid Research in Engineering Technology & Applied Science, 3(6), 1–5. https://ijrretas.com/research-paper/a-hybrid-recommendation-system-based-on-collaborative-filtering-and-association-rule