## 3 System Design Overview
### Functional Components
#### 3.2.2 Innovator-Expert Matchmaking  
The matchmaking engine is a foundational element of the platform, designed to establish connections between innovators and mentors using natural language processing and machine learning algorithms. The engine evaluates several parameters, such as project requirements, expertise areas, reputation scores, and semantic details from proposals. This ensures the matches it generates are relevant. A key strength of the matchmaking engine lies in its modular design. Each module represents a specialized approach to matchmaking, enabling a diverse range of techniques to be applied depending on the specific requirements of the task. These modules can be employed interchangeably or combined into hybrid solutions, providing flexibility and adaptability. The following paragraphs delve into the core modules of the matchmaking component, each contributing a unique capability to enhance the precision and effectiveness of the platform. One foundational technique utilized in the matchmaking system is term frequency-inverse document frequency (TF-IDF), a classic approach to text analysis. This method transforms textual data into numerical vectors based on the frequency of keywords, enabling the system to compute the similarity between proposals and expert profiles using cosine similarity. By quantifying the angular relationship between the vectors, cosine similarity ensures that matches are not influenced by the magnitude of the documents. Despite its simplicity, TF-IDF remains an indispensable component, particularly in contexts where computational efficiency is critical, or when keyword alignment is important. However, it is inherently limited in its ability to discern semantic relationships or account for variations in language. As such, while TF-IDF is well-suited for straightforward matching tasks, it lacks the depth required for more nuanced scenarios. Advancing beyond keyword-based approaches, bidirectional encoder representations from transformers (BERT) bring significant innovation to the matchmaking process. Unlike traditional models, BERT captures the contextual relationships of words within a sentence by analyzing them in their bidirectional context. This results in dense embeddings that encapsulate the deeper semantic meaning of the text. By applying cosine similarity to these embeddings, the system is able to identify matches that align not only in terminology but also in context and intent. The use of BERT is necessary for the matchmaking engine, as it ensures that the platform can recognize and connect related concepts even when they are expressed in different terms. This context-aware matching capability positions BERT as a critical tool in scenarios demanding precision and semantic depth. Building on the foundation of BERT, Sentence-BERT (S-BERT) introduces optimizations tailored for sentence-level comparisons. S-BERT generates embeddings that are specifically designed for the rapid calculation of similarity between short text segments, such as proposal descriptions and expert profiles. As with BERT, cosine similarity is employed to measure alignment, but S-BERT achieves this with significantly improved efficiency. The enhanced speed and computational efficiency of S-BERT make it particularly well-suited for real-time applications where responsiveness is key. Its fine-tuning for semantic textual similarity tasks further ensures that even subtle differences in language are captured with precision, enhancing the overall performance of the matchmaking engine. An alternative approach within the matchmaking system is provided by latent Dirichlet allocation (LDA), which focuses on topic modeling rather than direct text comparison. LDA decomposes text into a mixture of topics, identifying overarching themes that can serve as the basis for matching. By aligning proposals and expert profiles based on thematic relevance, LDA offers a broader perspective that complements the more granular techniques used elsewhere in the system.