# Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans
## 1 Introduction
By evaluating social connections, user activity, and content interactions, these systems can suggest new connections, tailor material feeds, and offer content/items that are likely to interest the user. This enhances the user experience by making the platforms more relevant and interesting, and it also promotes deeper user participation and interaction. Traditional recommendation systems use collaborative filtering, content-based filtering, and hybrid approaches [13]. Content-based filtering creates a recommendation model from item content. It proposes goods based on the user's profile and past decisions. This content-based strategy adapts quickly to new profiles. Its constraints include the need for a well-organized user profile to make a suggestion [14]. Collaborative filtering, another old method, relies on user preferences to make suggestions. The model assesses user similarity and groups users as neighbors [15]. Items are recommended to people with similar tastes. Finally, the hybrid approach uses collaborative, content-based filtering, and other strategies to improve its suggestion quality [16]. These algorithms usually work well, although data sparsity, scalability, cold starts, and other issues arise [17]. Collaborative filtering algorithms, which use an object's average rating to compare user ratings and make new suggestions, are the most popular. For analyzing big item sets, data sparsity is crucial, but there are various drawbacks. Another constraint is the closest neighbor prediction difficulty. The third barrier is scalability, which becomes a difficulty as the numbers of usersand items increase. A fourth constraint is the cold start problem, caused by a lack of user relationships and information [18]. Many academics have used deep learning, a cutting-edge method for complex learning and predicting tasks, to overcome these issues. We designed a deep learning-based movie recommendation system to alter CF in this study. CF has two learning models, influenced by model-based and memory-based techniques [19]. This study uses a succession of movies and user data to improve recommendation system models. It addresses the issues discussed earlier. Our goal is to provide users with a personalized experience and enhance the accuracy of the system. To make a good RSs, we use the advanced deep learning model's strong point--the transformer architecture, which is good at working with sequential data--to pull out temporal dependencies by looking at a user's past movie ratings and adding demographic information to look for correlations for people who are new to the system. This method provides users with personalized movie ideas and accurate recommendation tailor to their preferences. It achieves this by employing a mix of data mining and advanced techniques to make a list of movies similar to those they enjoy. The KMeans clustering technique helps to find hidden trends in movie features that are not always obvious. It also improves the user experience, makes recommendations more diverse, and increases efficiency. The main points of our manuscript are as follows:  
* We interpret the recommendation model using the sequential temporal of user interactions (movie ratings) to deliver a dynamic and contextual comprehension of user preferences based on the Movielens dataset. Our approach adopts a Transformer architecture, integrating multi-head attention with user demographic and movie embeddings, which allows the model to weigh various aspects of a user's movie-watching history differently when making predictions for the next target movie. Sequential recommendations are referred to as advanced model-based CF, as this is more effective in tackling the issues with existing traditional techniques. * Then, the model contains a KMeans clustering post process to group movies into clusters depending on their embeddings, which aids in diversifying recommendations. It also integrates movie genres as extra attributes, boosting the model's capacity to represent varied movie qualities.