In this project, I've developed a search solution tailored for online book publishers to enhance their readers' experience. This solution allows readers to find relevant chapters by understanding the semantic meaning of their queries, rather than relying on exact keyword matches.
- Semantic Search: Leverages the semantic meaning in queries to find the most relevant chapters.
- Tools and Libraries: Utilizes
nltk
,gensim
, andsklearn
to build the search model and retrieve top search results.
- NLTK (Natural Language Toolkit): For text preprocessing and tokenization.
- Gensim: For training Word2Vec models to capture semantic relationships between words.
- Scikit-learn (sklearn): For calculating similarities and ranking the search results.
This search solution provides a more intuitive and effective way for readers to find content, making it easier to navigate through extensive book collections based on the meaning of their queries.