Book Recommendation System based on Course Descriptions using Cosine Similarity

Ensuring the retrieval of books that match users' preferences is of paramount importance. A significant challenge users encounter is uncertainty regarding their choice of search terms, often stemming from a limited understanding of the content or exposure to new concepts. Offering users results that closely resemble their query represents one potential solution. This research aims to suggest books relevant to students' course topics, utilizing cosine similarity to compute similarity values within each document in the collection. Performance evaluation using a similarity threshold greater than 0.1 revealed that the retrieved book results achieved an average precision of 0.7 and a recall value of 0.73, indicating substantial alignment with the search terms. The anticipated benefits of the recommendation system encompass the elimination of the need for manual book suggestions by staff, the provision of personalized book recommendations tailored to readers' preferences, a deeper understanding of library user behavior, and the effective promotion of new books that align with users' interests.