# Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans
## 5 Conclusions  
This work offers a unique sequence movie recommendation system that employs deep learning techniques to enhance user experiences by providing tailored movie suggestions based on the captured sequential relations. Our study mainly focuses on user demographics and user-item interactions in a sequence to help overcome the issues in standard algorithms' recommendations caused by cold-start and data sparsity or scalability. We used user demographic and movie sequence embedding as input and added this to the transformer architecture to handle sequential data to generate the next target movie.  
The transformer model in our system exploits movie sequence embeddings, translating these dense vectors into a multi-head attention mechanism. This comprehensive technique helps increase the understanding of user behavior dynamics. It interacts with a multilayer perceptron (MLP) fed by user demographic data to discover intricate relationships, hence refining the personalized recommendations for the next target movie. Additionally, we applied KMeans clustering to detect underlying patterns in movie qualities that may not be visible from the anticipated ratings post-model training. This concept enables our system to widen the range of choices by suggesting movies from various clusters with comparable genre features. This technique considerably enhances the personalized Top-N movie recommendations, matching them with the user's essential preferences, including those that are not immediately apparent.  
In our studies, we conducted detailed assessments utilizing the MovieLens datasets (100 K and 1 M), which include essential variables such as movie ID, user ID, genres, gender, age, and employment. The findings demonstrate that our recommendation system (RS) model outperforms state-of-the-art baseline methods in terms of RMSE, MAE, precision, recall, and F1 scores, recording values of 1.0756, 0.8741, 0.5516, 0.3260, 0.4098 for the 100 K dataset and 0.9927, 0.8007, 0.5838, 0.4723, and 0.5222 for the 1 M dataset. Additionally, we examined the model, utilizing hit rate and normalized discounted cumulative gain (NDCG) measures, such as Hit Rate@5, Hit Rate@10, NDCG@5, and NDCG@10, with scores of 0.734, 0.7309, 0.5869, and 0.6238, respectively. These values represent improvements of 4.17%, 4.36%, 30.80%, and 29.25% compared to the 1 M dataset. Similarly, for the MovieLens 100 K dataset, the model obtains Hit Rate@5, Hit Rate@10, NDCG@5, and NDCG@10  
Figure 5: Training and validation loss performances of our proposed model on both datasets.  
with scores of 0.5676, 0.6633, 0.6925, 0.3714, and 0.3783, showing gains of 1.17%, \(-\)4.21%, 55.39%, and 27.58% over the baseline model. These results illustrate the higher performance and accuracy of our method when compared to other techniques, such as BPR, NeuMF, GRU4 Rec, Caser, and SaSRec, especially with extensive datasets.  
In future studies, we seek to expand our model by including more large-scale datasets to further evaluate its efficacy across multiple domains. Our key goal is to develop the system's capacity to provide more accurate and relevant suggestions to the target audience. This expansion will allow us to examine the model's adaptability and effectiveness in multiple circumstances, ensuring that our recommendations remain topical and beneficial to users with different tastes and needs.  
Conceptualization, S.S., S.P., M.K. and D.-S.P.; methodology, S.S. and D.-S.P.; software, S.S. and S.P.; writing--original draft preparation, S.S. and S.P.; writing--review and editing, D.-S.P.; visualization, S.S. and S.I.; supervision, D.-S.P. and M.K.; funding acquisition, D.-S.P. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Research Foundation of Korea, grant number, NRF-2022R1A2C1005921) and BK21 FOUR (Fostering Outstanding Universities for Research, grant number, 519990914048).  
Not applicable.