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: