Social Media Data Analysis using Recommendation Algorithms
S. Sathyavathi1, K.R. Baskaran2, S. Kavitha3
1S. Sathyavathi, Assistant Professor, Department of Information Technology, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
2Dr. K.R. Baskaran, Professor, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
3S. Kavitha, Assistant Professor, Department of Information Technology, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 06 October 2019 | Revised Manuscript received on 20 October 2019 | Manuscript Published on 26 December 2019 | PP: 394-397 | Volume-8 Issue-12S October 2019 | Retrieval Number: L109810812S19/2019©BEIESP | DOI: 10.35940/ijitee.L1098.10812S19
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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 frameworks (RSs) are utilized in application areas to help clients in the quest for their preferred items .Recommender system filters information which takes users ratings and predict user preferences in ecommerce and other categorical websites. We examine individual proposal dependent on client inclinations and search the neighbors through the client inclinations. It generates recommendations based on implicit feedback or explicit feedback. Implicit feedback is based on analysis of browsing patterns of the user. Express criticism is produced from the appraisals given by the client. All the more extensively tended to was the subject of AI’s calculations, centered around separating calculations dependent on the clients or questions, and dependent on substance.
Keywords: Recommendation System, Text Mining, Decision Making, Content-based Filtering (CBF).
Scope of the Article: Data Analytics