recommendation-system

Assignment 04

Book Recommendation System using Apache Spark and Flask

1. Preparation

1.1 Requirements

1. Apache Spark 2.4.0 Binary
2. PySpark 2.4.2 (Apache Spark Python API)
3. Jupyter Notebook
4. Numpy 1.16.3
5. Flask
6. Postman

1.2 Dataset

• Dataset's name: Goodbooks-10k Datasets
• Description : This dataset contains six million ratings for ten thousand most popular books (with most ratings). There are a few types of data here:
  • Explicit ratings
  • Implicit feedback indicators (books marked to read)
  • Tabular data or metadata (book info)
  • Tags
• Since we only need explicit ratings and book's metadata, so, we'll only use two kinds of data i.e. ratings.csv and books.csv

2. Description

Book Recommendation System is implemented using collaborative filtering with Spark's Alternating Least Squares (ALS) algorithm and Flask Python micro-framework. To make it simpler, the documentation will be divided into two parts:

Part 1 : Build a recommender engine using ALS Algorithm

Part 2 : Build a web-service on top of Spark models using Flask

References

• https://spark.apache.org/docs/latest/ml-collaborative-filtering.html
• https://github.com/jadianes/spark-movie-lens/blob/master/notebooks/online-recommendations.ipynb
• https://www.codementor.io/jadianes/building-a-recommender-with-apache-spark-python-example-app-part1-du1083qbw