Open Access
Subscription Access
Open Access
Subscription Access
Prioritizing the Marketing Start Ups using Classification Algorithm
Subscribe/Renew Journal
Surveying the improvements and working of the available products in the global markets and establishing the new products is a hard job for in the current technology improving world, instead lot more market survey concerns are there to perform this task and provide the exact report. In existing system, a typical CF- based recommender system associates a user with a group of like-minded users based on their individual preferences over all the items, either explicit or implicit, and then recommends to the user some unobserved items enjoyed by the group. In the proposed system a new website is created to gather the information about the new products. After gathering the comments using classification algorithm ratings will be provided as per the products list.
Keywords
Recommender System, Classification algorithm.
Subscription
Login to verify subscription
User
Font Size
Information
- Björn Brodén, Mikael Hammar, Bengt J. Nilsson, Dimitris Paraschakis. A Bandit-Based Ensemble Framework for Exploration/Exploitation of Diverse Recommendation Components: An Experimental Study within ECommerce [J]. ACM Transactions on Interactive Intelligent Systems, 10(1):4:1-4:32, 2020.
- Jiajun Bu, Xin Shan, Bin Xu, Chun Chen, Xiaofei He, Deng Cai. “Improving Collaborative Recommendation via UserItem Subgroups”, IEEE Transactions on Knowledge and Data Engineering, 2016.
- Joseph Ochieng Onginjo, Dong Mei Zhou, Tesema Fiseha Berhanu, Sime Welde Gebrile Belihu. “Analyzing the impact of social capital on US based Kickstarter prijects outcome”, Heliyon, 2021.
- Kanimozhi S, Durgha C, Jeya Sri B, Megadharshini M Sruthi B, “Efficient System For Complaint Portal Management Using Classification Technique In Data Analytics”, International Journal Of Psychosocial Rehabilitation, Vol 24, Issue 3 March 2020, pp.4167-4171.
- Mohit Sharma, F. Maxwell Harper, George Karypis. Learning from Sets of Items in Recommender Systems [J]. ACM transactions on Interactive Intelligent Systems, 9(4):19:1-19:26, 2019.
- Rachida Ihya, Abdelwahed Namir, Sanaa EI Filali, Mohammed Ait Daoud, Fatima Zahra Guerss. “J48 algorithms of machine learning for predicting user’s the acceptance of an E-orientation systems”, Proceedings of the 4 th International Conference on Smart City Applications – SCA’119, 2019.
- S.Kanimozhi and Padmini Devi, “A Novel Approach for Deep Learning Techniques using Information Retrieval from Bigdata. International Journal of Pure and Applied Mathematics., Vol.118, no.8 1314-3395, 2018.
- Singh, Pradeep & Dutta Pramanik, Pijush & Dey, Avick & Choudhury, Prasenjit. (2021). Recommender Systems: An Overview, Research Trends, and Future Directions. International Journal of Business and Systems Research. 15. 14–52.
- Thi Ngoc Trang Tran, Alexander Felfernig, Nava Tintarev. Humanized Recommender Systems: State-of-the-art and Research Issues [J]. ACM Transactions on Interactive Intelligent Systems, 11(2):9:1-9:41, 2021.
- Yu Zhenhai, Fang Yonghao, Zhang Yikun, Liu Shufen. “The Research of Modified Collaborative Filtering Recommendation Algorithm”, 2015 7th International Conference on Information Technology in Medicine and Education (ITME), 2015.
Abstract Views: 277
PDF Views: 0