Build Your Own Book Recommender Using LLMs and Python

Build Your Own Book Recommender Using LLMs and Python

In this hands-on course, you’ll learn how to build a smart, personalized book recommender system using cutting-edge AI tools. Instead of relying on traditional collaborative filtering, you’ll leverage large language models (LLMs), vector databases, and natural language processing (NLP) techniques.

By the end of this course, you’ll have a working book recommender that can understand user preferences, detect sentiment in reviews, classify books by genre using zero-shot classification, and suggest relevant titles – all using Python.

Prerequisites

  • Python programming (intermediate level)
  • Basic knowledge of APIs and JSON
  • Familiarity with machine learning concepts is a plus
  • No prior experience with LLMs is required

Learning Outcomes

By the end of this course, you will:

  • Understand the principles of vector search and embeddings
  • Use LLMs to extract insights from book summaries and user input
  • Build a smart recommendation engine using sentence similarity
  • Classify books into genres using zero-shot learning
  • Perform sentiment analysis on user reviews
  • Deploy your recommender as a simple API or web app

Course Modules

Module 1: Introduction to Book Recommendation Systems

  • What is a recommendation system?
  • Limitations of traditional recommenders
  • The power of LLMs and NLP
  • Overview of the system we’ll build

Module 2: Setting Up the Environment

  • Installing Python libraries: transformers, sentence-transformers, scikit-learn, gradio, pinecone/FAISS
  • Introduction to Hugging Face and OpenAI APIs
  • Creating your development workspace (Jupyter or VS Code)

Module 3: Collecting and Preparing Book Data

  • Finding or scraping book summaries and metadata
  • Data cleaning and preprocessing
  • Creating embeddings using sentence-transformers
  • Storing embeddings in FAISS or Pinecone

Module 4: Understanding Vector Search

  • Introduction to vector databases
  • Cosine similarity vs Euclidean distance
  • Querying the vector store with user input
  • Returning top N similar books

Module 5: Zero-Shot Classification for Genre Detection

  • What is zero-shot classification?
  • Using Hugging Face’s facebook/bart-large-mnli or similar
  • Classifying books into genres without labeled data
  • Visualizing genre distribution of your dataset

Module 6: Sentiment Analysis on Reviews

  • Using pre-trained models for sentiment analysis
  • Analyzing real or synthetic user reviews
  • Integrating sentiment scores into recommendations
  • Optional: fine-tuning on domain-specific reviews

Module 7: LLMs for Personalized Suggestions

  • Using OpenAI or Hugging Face LLMs to interpret user preferences
  • Prompt engineering for refining recommendations
  • Example: “I like fast-paced thrillers with strong female leads”
  • Generating natural-language responses

Module 8: Building the Recommender Engine

  • Combining vector search, genre detection, sentiment analysis
  • Designing the recommendation logic
  • Creating a backend with Flask or FastAPI
  • Exposing recommendations via API

Module 9: Deploying the App

  • Building a frontend using Gradio or Streamlit
  • Hosting your app on Hugging Face Spaces or Render
  • Adding search and feedback features
  • Logging usage and metrics

Module 10: Wrap-up & Capstone Project

  • Review of all concepts
  • Capstone: Build and deploy your own recommender for:
    • Novels
    • Business books
    • Children’s books, etc.

Module 1: Introduction & Setup

Step 1: Install Required Libraries

bashCopyEditpip install openai faiss-cpu pandas numpy scikit-learn transformers streamlit

Step 2: Get API Keys

Module 2: Load and Prepare Book Data

Step 1: Load a Book Dataset

Use public datasets like:

pythonCopyEditfrom datasets import load_dataset

dataset = load_dataset("yelp_review_full")  # Use for demo or mock book reviews

Or load a CSV:

pythonCopyEditimport pandas as pd

df = pd.read_csv("books.csv")  # columns: title, author, description, genre

Step 2: Clean and Prepare Text

pythonCopyEditdf['clean_description'] = df['description'].apply(lambda x: x.strip().lower())

Module 3: Generate Embeddings

Step 1: Choose Embedding Model

pythonCopyEditfrom transformers import AutoTokenizer, AutoModel
import torch

tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")

Step 2: Generate Vector Embeddings

pythonCopyEditdef get_embedding(text):
    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
    with torch.no_grad():
        output = model(**inputs)
    return output.last_hidden_state.mean(dim=1).squeeze().numpy()

df['embedding'] = df['clean_description'].apply(get_embedding)

Module 4: Build Vector Search with FAISS

Step 1: Create FAISS Index

pythonCopyEditimport faiss
import numpy as np

embedding_matrix = np.vstack(df['embedding'].values)
index = faiss.IndexFlatL2(embedding_matrix.shape[1])
index.add(embedding_matrix)

Step 2: Recommend Books

pythonCopyEditdef recommend_books(query, k=5):
    query_vec = get_embedding(query).reshape(1, -1)
    distances, indices = index.search(query_vec, k)
    return df.iloc[indices[0]]

print(recommend_books("space adventure novel"))

Module 5: Zero-Shot Classification

Use LLM to Classify Book Genre

pythonCopyEditfrom transformers import pipeline

classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
labels = ["Romance", "Science Fiction", "Mystery", "Fantasy", "Biography"]

def classify_book(text):
    return classifier(text, labels)

print(classify_book("A gripping tale of interstellar war and alien politics."))

Module 6: Sentiment Analysis on Reviews

pythonCopyEditsentiment_analyzer = pipeline("sentiment-analysis")

def analyze_sentiment(text):
    return sentiment_analyzer(text)

print(analyze_sentiment("I loved this book! It was an emotional rollercoaster."))

Module 7: Create a Web App (Optional)

Option A: Streamlit App

pythonCopyEditimport streamlit as st

st.title("Smart Book Recommender")

query = st.text_input("Describe the book you're looking for:")
if query:
    results = recommend_books(query)
    for i, row in results.iterrows():
        st.write(f"**{row['title']}** by {row['author']}")
        st.write(row['description'])

Option B: Flask API (Basic Example)

pythonCopyEditfrom flask import Flask, request, jsonify

app = Flask(__name__)

@app.route("/recommend", methods=["POST"])
def recommend():
    data = request.get_json()
    query = data['query']
    results = recommend_books(query)
    return jsonify(results.to_dict(orient="records"))

app.run(port=5000)

Module 8: Final Project & Deployment

  • Prepare a demo app using Streamlit Cloud or Render.
  • Document your project.
  • Upload to GitHub or create a personal portfolio project.

Final Project Deliverables

  • A working book recommender that takes user prompts and returns top book suggestions.
  • Zero-shot genre classification.
  • Sentiment scoring of book reviews.
  • Deployment-ready web interface (Streamlit or Flask).

Would you like me to help generate the source code, create a GitHub README, or prepare this as a downloadable PDF course package?

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