Sanzida Akter
ABSTRACT
In this fast-paced world, it is important for each individual to have some form of entertainment that can help them rejuvenate and regain their energy. The reliance we acquire from entertainment allows us to work harder and enthusiastically. A movie can be one of the finest sources of this entertainment. But finding a good movie can be hectic sometimes. A movie recommendation system can provide a helpful solution to the problem of searching for preferred movies from a vast array of options. By utilizing such a system, one can easily discover movies that match their preferences, which saves time and reduces stress associated with the search process. As a result, it is essential that the system for suggesting movies to us is very trustworthy and gives us recommendations for the films that are either most similar to or identical to our tastes. This movie recommendation system is established using K-Nearest Neighbour and cosine similarity. The cosine similarity method is capable of bringing together documents that are similar, even if they have a large Euclidean distance between them because of their size. Moreover, the KNN algorithm, which is highly accurate in making predictions, can compete with other precise models. It is utilized to identify groups of individuals with similar movie rating preferences, and predictions are computed by taking an average of the highest k neighboring ratings.
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Index Terms
- Bengali Movie Recommendation System using K Nearest Neighbor and Cosine Similarity
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