Collaborative Filtering Recommender System for Financial Market
F.R.Sayyed1, R.V.Argiddi2, S.S.Apte3
1F.R.Sayyed, Computer Science and Engineering Department, Solapur University, Walchand Institute of Technology, Solapur, Maharashtra, India.
2R.V.Argiddi, Computer Science and Engineering Department, Solapur University, Walchand Institute of Technology, Solapur, Maharashtra, India.
3S.S.Apte, Computer Science and Engineering Department, Solapur University Walchand Institute of Technology, Solapur, Maharashtra, India.
Manuscript received on July 20, 2013. | Revised Manuscript received on August 14, 2013. | Manuscript published on August 30, 2013. | PP: 389-391 | Volume-2, Issue-6, August 2013. | Retrieval Number: F2113082613/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recommender systems suggest items to users by utilizing the techniques of Collaborative filtering based on historical records of items that users have purchased. Recommender systems make use of data mining techniques to determine the similarity among a huge collection of data items, by analyzing historical user data and then extracting hidden useful information or patterns. Collaborative filtering aims at finding the relationships among the new individuals and the existing data items in order to further determine the similarity and provide recommendations. In this paper, a Collaborative Filtering Recommender System is proposed which can be used for financial markets such as stock exchanges for future predictions.
Keywords: Collaborative Filtering, Financial Markets, Recommender System, Stocks Predictions.