A voice-interactive book recommendation system powered by Natural Language Processing (NLP). This project takes user queries via speech, processes the input using NLP techniques, and recommends relevant books using content-based filtering methods such as TF-IDF vectorization and cosine similarity.
- Voice-based input: Users can speak their preferences or queries.
- NLP-powered engine: Preprocesses text for improved accuracy using:
- Tokenization
- Stop word removal
- Lemmatization
- TF-IDF Vectorization: Converts text into numerical feature vectors.
- Cosine Similarity Matching: Finds the most similar books based on user input.
- Content-Based Filtering: Recommends books based on description similarity, not ratings.