Movie Recommendation System using Term Frequency-Inverse Document Frequency and Cosine Similarity Method
N. Muthurasu1, Nandhini Rengaraj2, Kavitha Conjeevaram Mohan3
1N. Muthurasu, Assistant Professor, Department of Computer Science, SRM University, Vadapalani (Tamil Nadu), India.
2Nandhini Rengaraj, Department of Computer Science, SRM University, Vadapalani (Tamil Nadu), India.
3Kavitha Conjeevaram Mohan, Department of Computer Science, SRM University, Vadapalani (Tamil Nadu), India.
Manuscript received on 22 April 2019 | Revised Manuscript received on 01 May 2019 | Manuscript Published on 07 May 2019 | PP: 86-90 | Volume-7 Issue-6S3 April 2019 | Retrieval Number: F1018376S19/2019©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: Recommendation engines are trained to produce fast and coherent suggestions to users. This paper describes a hybrid video recommendation system using Term-frequency Inverse document frequency technique for vectorization. Cosine similarity method is used for similarity measure. The system is presented to the user through a web-hosted user-interface. The advantages of the system include efficient recommendations, correct suggestions even with a small data model. Future enhancements include user profiling and documentations, analytics reports for producers and users and data acquirement through web scraping.
Keywords: Movie Recommendation Systems, Cosine Similarity, TF-IDF.
Scope of the Article: Frequency Selective Surface