Retrieval of course candidates when searching via title.

Goal To optimize the retrieval of relevant course candidates based on similar course titles. Data WBS data for 2136 modules, including title, product category, education goal, and learning goal. Method/Approach A random sample of 100 titles from the dataset was used as queries. Vector search was done on two different fields: (1) Title and (2) Combined title and learning goal. Three embedding models were tested: SBERT, OpenAI text-embedding-3-small, OpenAI text-embedding-3-large. Vector search replicated locally with FAISS using the same OpenSearch parameters (M=24, ef_search=100, ef_construction=128). Results The OpenAI 3-large model had the best retrieval scores for both the title-only search (0.92 at k=6) and when combining the title and learning goal (0.85 at k=6). The OpenAI 3-small model wasn’t far behind. SBERT struggled in both cases, showing the lowest scores (0.57 and 0.48 at k=6). Evaluation Metrics Retrieval quality: Cumulative Gain (CG at k=1 to k=6) Conclusions Results highlight the better retrieval effectiveness of OpenAI text embedding model 3-large, particularly at higher ranks, when searching on title and learning goal combined. In contrast, including the learning goal slightly decreased SBERT’s performance, possibly due to noise or irrelevant information that the embeddings struggled to handle effectively.