## 2 Literature Review
### Cf
Recent studies have also explored the application of CF in health and wellness, such as in personalized health recommendations. [PERSON] et al. [52] devised an intelligent health recommender system using a deep learning approach that integrated a restricted Boltzmann machine (RBM) with a convolutional neural network (CNN). The efficacy of this model was gauged through performance metrics like mean absolute error and root square mean error. Their results indicated that the RBM-CNN model was more effective, with fewer errors compared to other approaches. More sophisticated techniques are also being employed to improve CF. [PERSON] and [PERSON] [53] designed a CF model utilizing a graph-based framework to capture the relationship between users and items, as well as to model user priorities. Their distinctive method enabled the formation of user profiles that encompassed both historical and present interests. Moreover, their model showcased enhanced results over standard CF techniques in various metrics. The integration of contextual information into CF has been a rising trend to enhance its efficacy. A notable advancement in this domain was made by [PERSON] et al. [25]. They refined a hybrid collaborative filtering mechanism by harnessing both tags and temporal factors. In their method, tag information was exhaustively utilized to represent users and items. Moreover, while determining similarities between users or items, both rating and tag data were considered. Notably, they infused a time-rating aspect to capture user interests more accurately. Their findings indicate that their approach can effectively address data sparsity issues and consistently deliver accurate predictions. Big data analytics has further revolutionized the field of CF. [PERSON] et al. [50], and [PERSON] et al. [51] demonstrated this by applying their algorithms to big data, thereby validating the methodologies. The latter utilized a MapReduce framework in combination with an item-based recommendation algorithm, resulting in improved efficiency and accuracy. Similarly, [PERSON] et al. [54] proposed a multi-criteria CF for hotel recommendations, incorporating dimensionality reduction with predictive machine learning techniques to boost scalability. The model was validated using a big dataset and demonstrated robust performance. In another groundbreaking study, [PERSON] et al. [55] presented a CF model that incorporated a network representation learning framework for citation recommendation. This approach was tested on scholarly big data and was shown to outperform other techniques according to several evaluation metrics. In conclusion, the landscape of CF-based recommendation systems is continually evolving, spurred by advancements in deep learning, representation learning, the integration of auxiliary information, and techniques for addressing challenges such as data sparsity and computational costs. The implementation of these sophisticated methods, when coupled with big data analytics, has the potential to further revolutionize this domain. Despite the impressive progress already made, there remain numerous opportunities for innovation and refinement, promising a future of increasingly personalized, efficient, and accurate recommendation systems.