## 4 Foundations of Recommendation Systems and LLM Integration
### Traditional Recommender Systems  
Conventional recommender systems typically employ one of three strategies: collaborative filtering, content-based filtering, or hybrid approaches.  
Collaborative filtering is one of the most widely used approaches in conventional recommendation algorithms. It is based on the premise that users who have interactions with or preferred similar items will behave similarly in the future. This technique can be divided into two forms: user-based [13] and item-based.  
By assuming that users will have similar preferences based on similar ratings or interactions, user-based collaborative filtering [13] identifies users whose profiles closely match those of a target user. This method makes it feasible to calculate a product's rating by looking at the ratings of other users. The user-item interaction matrix's sparsity, however, makes it difficult to find comparable users who can offer trustworthy recommendations. Assuming that users who have rated one item are likely to have rated other related items, item-based collaborative filtering is different from user-based collaborative filtering in that it concentrates on the relationships between items. Since there is typically more data regarding interactions with items than with specific users, this method is less vulnerable to sparsity problems.  
Content-based filtering: While collaborative filtering relies on user-item interaction data, content-based filtering uses the attributes of items and users to make recommendations. Analysing item characteristics and user preferences helps content-based filtering to generate recommendations. In cold-start situations it works well and can add new objects without user involvement. It can, however, suffer from overspecialisation and depends on thorough item attribute information, which might not always be accessible. For some situations, content-based filtering presents a more flexible solution overall [14].  
Hybrid filtering was introduced to mitigate the weaknesses of collaborative and content-based filtering. These systems combine the strengths of both approaches, leading to more accurate and robust recommendations. A typical hybrid approach blends collaborative filtering with content-based methods, leveraging user interaction data and item attributes for prediction. A notable example of a hybrid recommender system is Netflix's recommendation engine. Netflix uses a combination of collaborative filtering, content-based filtering, and business rules to provide personalized recommendations to its users. This hybrid system allows Netflix to provide accurate recommendations even in cases where the data from the user-item interaction is sparse or incomplete [15]. Hybrid systems are highly versatile, but they also introduce challenges in terms of complexity. Combining multiple models requires significant computational resources, mainly when processing large-scale datasets. Designing a hybrid system that effectively integrates different techniques without redundancy is also complex.  
Traditional recommender system architectures face several critical challenges including sparsity [13], scalability [16], cold-start problems [14], static preferences, and lack of interpretability [16].