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movie_analyzer_recommender's Introduction

movie_recommander

This is the individual project from EPFL's system for data management course. a more detailed project discription can be found at: chrome-extension://hmigninkgibhdckiaphhmbgcghochdjc/pdfjs/web/viewer.html?file=https%3A%2F%2Fmoodle.epfl.ch%2Fpluginfile.php%2F3171624%2Fmod_resource%2Fcontent%2F4%2FCS460_2023_project-5.pdf

In this project, we are expected to implement data processing pipelines overApache Spark. We are given a dataset based on the MovieLens datasets. The dataset includes:

  • A set of movies (e.g., movies, TV series) and their genres
  • A log of "user rates movie" actions A video streaming application requires large-scale data processing over the given dataset. The application has a user-facing component (which we are not concerned with) that serves recommendations and interesting statistics to the end-users. Specifically, for each title, it displays:
  • All the movies that are "similar" in terms of keywords
  • An average rating In the background, the application issues Spark jobs that pre-compute the information that is served. Our task is to write Spark code for the required functionality.

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