Recommendation System for Movies Using Improved version of SOM with Hybrid Filtering Methods

Many industries uses recommendation systems (RS) to iden-tify product recommendations when users actively participate on e-commerce sites. Recently, massive growth in both goods and consumers has faced serious challenges. Numerous websites present the consumer with numerous options at once, creating a lot of confusion. In addition, finding the right product or active user is an essential part of RS. Products are already recommended based on consumer preferences and sociodemo-graphic trends. A hybrid-action-related recommendation based on K-Nearest Neighbor Similarity (HAR-KNN) combines the ease of hybrid filtering with the development of feature vectors to improve the user behaviour matrix. To categorize attributes, it uses both quality and quantity classifiers. Additionally, the proposed methodology overcomes shortcomings in earlier approaches to evaluating user preference for goods and feature analysis. The SOM AND KNN classification technique has been approved for the purpose of locating information about user behavior online and in real time for a specific user group containing a large amount of data in re-lation to the commonalities among many users and target users. A test result is evaluated by using highly predictive metrics such as Precision (P), Recall (R), F, as well as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE).INDEX TERMS Recommendation System (RS), User behaviour data, Hy-brid filtering, KNN, behavioural matrix.