Vishwas Patel
ABSTRACT
With the increase in E-commerce, Recommendation Systems are getting popular to provide recommendations of various items (movies, books, music) to users. To build the Recommendation System (RS), Collaborative Filtering (CF) techniques are proven efficient. From the main two Collaborative Filtering techniques i.e. User-Based and Item-Based, survey suggest that Item-Based CF provides better recommendations. A novel approach, Ratio-Based CF provides recommendation depending upon the item's ratio is more accurate comparatively but face scalability problem. To overcome this problem a parallel approach can be used instead of sequential. Our experiments shows that Ratio Based CF techniques have more accuracy comparatively as well as Parallel (Hadoop) implementation of Ratio Based CF Techniques have drastically reduce the training time (i.e. ratio calculating time between each pair of items) from 90 minutes in Java to 5 minutes in Hadoop for sub-data of MovieLens 100K dataset.
- Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009): 4. Google Scholar
Digital Library
- Walunj, Sachin Gulabrao, and Kishor Sadafale. "An online recommendation system for e-commerce based on apache mahout framework." InProceedings of the 2013 annual conference on Computers and people research, pp. 153--158. ACM, 2013. Google ScholarDigital Library
- Chen, DanEr. "The collaborative filtering recommendation algorithm based on BP neural networks." In Intelligent Ubiquitous Computing and Education, 2009 International Symposium on, pp. 234--236. IEEE, 2009.. Google ScholarDigital Library
- Patil, Vandana A., and Lata Ragha. "Comparing performance of collaborative filtering algorithms." In Communication, Information & Computing Technology (ICCICT), 2012 International Conference on, pp. 1--6. IEEE, 2012.Google Scholar
- Liu, Qiang, Bingfei Cheng, and Congfu Xu. "Collaborative Filtering Based on Star Users." In Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on, pp. 223--228. IEEE, 2011. Google ScholarDigital Library
- Sarwar, Badrul, George Karypis, Joseph Konstan, and John Riedl. "Item-based collaborative filtering recommendation algorithms." In Proceedings of the 10th international conference on World Wide Web, pp. 285--295. ACM, 2001. Google ScholarDigital Library
- Liu, Yaqiu, Zhendi Wang, and Man Li. "Ratio-based collaborative filtering algorithms." In Systems and Control in Aerospace and Astronautics, 2008. ISSCAA 2008. 2nd International Symposium on, pp. 1--5. IEEE, 2008.Google ScholarCross Ref
- Zhang, Y. L., M. M. Ma, and S. P. Wang. "Research of User-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop." (2015).Google Scholar
- Zhao, Zhi-Dan, and Ming-Sheng Shang. "User-based collaborative-filtering recommendation algorithms on hadoop." In Knowledge Discovery and Data Mining, 2010. WKDD'10. Third International Conference on, pp. 478--481. IEEE, 2010. Google ScholarDigital Library
- Bamnote, G. R., and S. S. Agrawal. "Evaluating and Implementing Collaborative Filtering Systems Using Apache Mahout." In Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on, pp. 858--862. IEEE, 2015. Google ScholarDigital Library
- Kumar, Thangavel Senthil, and Swati Pandey. "Customization of Recommendation System Using Collaborative Filtering Algorithm on Cloud Using Mahout." In Intelligent Distributed Computing, pp. 1--10. Springer International Publishing, 2015.Google Scholar
- Jiang, Jing, Jie Lu, Guangquan Zhang, and Guodong Long. "Scaling-up item-based collaborative filtering recommendation algorithm based on hadoop." In Services (SERVICES), 2011 IEEE World Congress on, pp. 490--497. IEEE, 2011. Google ScholarDigital Library
- https://en.wikipedia.org/wiki/Recommender_systemGoogle Scholar
- Goldberg, David, David Nichols, Brian M. Oki, and Douglas Terry. "Using collaborative filtering to weave an information tapestry." Communications of the ACM 35, no. 12 (1992): 61--70. Google ScholarDigital Library
- Miller, Bradley N., Istvan Albert, Shyong K. Lam, Joseph A. Konstan, and John Riedl. "MovieLens unplugged: experiences with an occasionally connected recommender system." In Proceedings of the 8th international conference on Intelligent user interfaces, pp. 263--266. ACM, 2003. Google ScholarDigital Library
- Linden, Greg, Brent Smith, and Jeremy York. "Amazon. com recommendations: Item-to-item collaborative filtering." Internet Computing, IEEE 7, no. 1 (2003): 76--80. Google ScholarDigital Library
- Parallel Ratio Based CF for Recommendation System
Recommendations
An effective recommendation method for cold start new users using trust and distrust networks
Recommendation systems analyze the purchasing behavior (e.g., item ratings) of users to learn about their preferences and recommend products or services that may be of interest to them. However, as new users require time to become familiar with ...
Generating Top-N Items Recommendation Set Using Collaborative, Content Based Filtering and Rating Variance
AbstractThe main purpose of any recommendation system is to recommend items of users’ interest. Mostly content and collaborative filtering are widely used recommendation systems. Matrix factorization technique is also used by many recommendation systems. ...
Finding group shilling in recommendation system
WWW '05: Special interest tracks and posters of the 14th international conference on World Wide WebIn the age of information explosion, recommendation system has been proved effective to cope with information overload in e-commerce area. However, unscrupulous producers shill the systems in many ways to make profit, and it makes the system imprecise ...
Comments