# Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans
## 2 Related Work
### Traditional-Based Recommendation Systems  
Several studies have investigated the evolution of recommendation systems, which traditionally comprise two parts: content-based filtering and collaborative filtering [20]. Content-based filtering (CBF) has focused on recommending new items that are similar to those a user liked in the past, relying on item features and user profiles. [PERSON], [PERSON] and [PERSON], [21] emphasized the importance of item features and user profile creation for effective recommendations. The strength of CBF lies in its ability to recommend items similar to those a user preferred in the past, utilizing detailed descriptions and metadata of items. However, it often suffers from a lack of diversity in its recommendationsand struggles with the new item (cold start) problem, where new items have not yet been rated sufficiently to be recommended.  
On the other hand, collaborative filtering (CF), the more prevalent approach, relies on both implicit and explicit user-item interactions [22]. CF utilized the user-item interaction matrix, introducing foundational algorithms like matrix factorization. Despite their effectiveness, challenges such as cold start, scalability, and sparsity issues persist. [PERSON], [PERSON] et al. [23] addressed scalability with \"item-based collaborative filtering\", proposing algorithms that improved the recommendation efficiency and performance. [PERSON], [PERSON] et al. [24] furthered CF with matrix factorization techniques, enhancing its ability to deal with large datasets and improving recommendation accuracy. [PERSON], [PERSON], and [PERSON] [25] proposed matrix factorization (MF) and Singular Value Decomposition (SVD) and decomposed the user-item interaction matrix into latent factors, capturing underlying patterns in user preferences and item characteristics to improve recommendation accuracy. [PERSON], [PERSON] et al. [26] utilized Bayesian Personalized Ranking (BPR) to optimize personalized ranking item predictions by extracting latent features representing user preferences and factorizing the user-item interaction matrix into a lower dimension based on explicit or implicit feedback. To overcome the limitations inherent in CBF and CF, researchers explored hybrid models. [PERSON], [PERSON] et al. [27] presented hybrid news recommendation algorithms that combine content-based methods (using TF-IDF and K-means clustering) with SVD-based collaborative filtering to improve overall performance. Further, [PERSON], [PERSON] et al. [28] conducted a comparative study of collaborative filtering in product recommendation, which demonstrated that the LightFM library outperforms the surprise library in handling foodservice transactional data, emphasizing LightFM's ability to deal with sparse data and establish personalized recommendations. This paper emphasizes the importance of hyperparameter tuning for optimal algorithm performance and represents a significant development in collaborative filtering techniques.  
Initially, recommendation systems relied heavily on content-based and collaborative filtering techniques. Although they are effective, they are limited in their ability to handle complex patterns and large-scale data, particularly when the rating matrix is sparse.