A1 – Recommendation System

Description

  • In assignment 1 you shall implement a recommendation system for the movies data set
  • You can use any programming language you like
  • You can work alone or in group of two students
  • You shall present your application and code at an oral examination

 

Requirements

Grade Requirements
E
  • Build a recommendation system that can find similar users and find recommendations for
    a user, using the movies large dataset (see Datasets page)
  • You can verify that your application works by using the example dataset from the lecture (see Datasets page)
  • Use Euclidean distance as similarity measure
  • Implement the system using a REST web service where:
     1) client sends a request to a server
     2) the server responds with json data
     3) the json data is decoded and presented in a client GUI
C-D
  • Implement the Pearson Correlation similarity measure
  • It shall be possible to select which measure to use from the client GUI
A-B
  • Implement functionality for pre-generating an Item-Based Collaborative Filtering table by
    transforming the original data set
  • Use the pre-generated table to implement a second way of finding recommendations for
    a user
  • You shall only use Euclidean distance as similarity measure
  • It shall be possible to select how to find recommendations from the client GUI
    (Item-Based or User-Based)

 

Test cases – small example dataset

Here are some test cases for the small example dataset you can use to verify that your system works correctly.

- Find recommended movies for user Mike using Euclidean distance:

- Find recommended movies for user Mike using Pearson similarity:

- Find recommended movies for user Mike using Item-based filtering:

Test cases – large dataset

Here are some test cases for the larger dataset you can use to verify that your system works correctly.

- Find recommended movies for user Angela using Euclidean distance:

- Find recommended movies for user Will using Pearson similarity:

- Find recommended movies for user Andy using Item-based filtering:

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