Abstract
Recommendation system has become of utmost importance during the last decade. It is due to the fact that a good recommender system can help assist people in their decision-making process on the daily basis. When it comes to movie, collaborative recommendation tries to assist the users by using help of similar type of users or movies from their common historical ratings. Genre is one of the major meta tag used to classify similar type of movies, as these genre are binary in nature they might not be the best way to recommend. In this paper, a hybrid model is proposed which utilizes genomic tags of movie coupled with the content-based filtering to recommend similar movies. It uses principal component analysis (PCA) and Pearson correlation techniques to reduce the tags which are redundant and show low proportion of variance, hence reducing the computation complexity. Initial results prove that genomic tags give the better result in terms of finding similar type of movies, and give more accurate and personalized recommendation as compared to existing models.
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Ali, S.M., Nayak, G.K., Lenka, R.K., Barik, R.K. (2018). Movie Recommendation System Using Genome Tags and Content-Based Filtering. In: Kolhe, M., Trivedi, M., Tiwari, S., Singh, V. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 38. Springer, Singapore. https://doi.org/10.1007/978-981-10-8360-0_8
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DOI: https://doi.org/10.1007/978-981-10-8360-0_8
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