## 2 Literature Review
### Cf  
CF recommender systems have evolved significantly over the years, with numerous studies proposing techniques to enhance their accuracy and efficiency. This literature review provides an overview of recent research findings in this domain, highlighting significant developments and trends. CF recommender systems are fundamentally driven by the belief that the opinions of others significantly influence buyer decisions [36]. Such systems typically utilize user-item matrices to make recommendations based on numerical weights [37]. In recent years, however, these systems have begun to leverage additional forms of user feedback, including click behaviors, purchase history, and weblogs, for preference indication [38]. Recent research has shown a growing interest in integrating contextual data into CF recommender systems. In this vein, [PERSON] [39] proposed a system that anticipates user preferences by examining the actions of users with similar inclinations, especially when operating in analogous contexts, thereby improving recommendation quality. [PERSON] et al. [40] integrated contextual factors, such as time and place, into their recommendation process, while [PERSON] et al. [41] demonstrated that the granularity of contextual information impacts the prediction of customer behavior. An emerging trend in recent studies is the utilization of user-generated tags to improve CF recommender systems. [PERSON] et al. [31] employed collaborative tagging to filter user preferences, offering enhanced recommendation quality. Similarly, [PERSON] et al. [32] introduced a personalized recommendation framework that harnesses the triadic relationships between users, objects, and tags. Deep learning has also been integrated into recommender systems, providing robust representations of users and items. [PERSON] et al. [42] noted that these developments steer recommendation algorithms away from conventional approaches based solely on user-item interactions and towards models incorporating auxiliary information. By offering a holistic understanding of user behavior and preferences, such models can potentially improve recommendation accuracy. [PERSON] et al. [43] emphasized the value of user-generated content, such as reviews, in enhancing recommendation quality. Notably, [PERSON] et al. [44] introduced the Neural Graph Collaborative Filtering (NGCF) recommendation system, a novel framework that seamlessly blends user-item interactions into the embedding phase. This design enhances the modeling of higher-order connectivity within user-item networks. However, concerns have been raised about the computational cost of using a multi-layer perceptron (MLP) for embedding combination [45]. Addressing data sparsity, a recurring challenge in CF, has been a focal point in recent research. [PERSON] et al. [46] suggested a Review-Based Matrix Factorization method that utilizes review texts as side information to improve recommendation accuracy. In parallel, [PERSON] et al. [47] addressed data sparsity by introducing neighborhood-enriched contrastive learning (NCL), which enhances performance by considering neighboring relationships among users and items. Moreover, [PERSON] et al. [48] presented hypergraph contrastive collaborative filtering (HCCF), a cutting-edge self-supervised recommendation framework designed to combat the over-smoothing effect and scarcity of supervision signals. [PERSON] [49] further investigated the roles of different similarity measures, such as ITR, IPWR, and AMI, in CF, noting that their performance varied depending on whether they were applied in user-based or item-based recommender systems. Studies have also demonstrated the successful application of CF in big data analytics. [PERSON] et al. [50] proposed CF algorithms using improved k-means clustering and principal component analysis, demonstrating superior performance over traditional CF algorithms. [PERSON] et al. [51] developed a MapReduce-based recommendation algorithm, indicating enhanced efficiency and accuracy when applied to big data.