This is the code for our RecSys '20 paper. Other materials can be found here:
This assumes that you have a recent Anaconda distribution of Python 3 installed. To install the dependencies,
conda env create -f environment.yml
Then, activate your new environment
conda activate item2item
and get the datasets
python 0_get_datasets.py
To run the command-line demo that allows you to retrieve item-to-item recommendations interactively,
python 1_run_demo.py
Then, follow the prompts
Input (partial) movie title [empty to quit]: toy
option #
0 Toy Story (1995)
1 Toy Story 2 (1999)
2 Toy Story 3 (2010)
3 Toys (1992)
4 Babes in Toyland (1961)
5 Toy Soldiers (1991)
6 Toy, The (1982)
7 Toy Story 4 (2019)
8 Babes in Toyland (1934)
9 Toy Story of Terror (2013)
Input option (0-10) [empty to exit]: 0
Recommendations for Toy Story (1995)
DebiasedModel ItemKNN
title score title score
0 Toy Story 2 (1999) -1.319431 Toy Story 2 (1999) 0.632260
1 Toy Story 3 (2010) -1.382858 Willy Wonka & the Chocolate Factory (1971) 0.554588
2 Finding Nemo (2003) -1.532166 Back to the Future (1985) 0.547485
3 Incredibles, The (2004) -1.544819 Monsters, Inc. (2001) 0.542195
4 Monsters, Inc. (2001) -1.571283 Lion King, The (1994) 0.541657
5 Shrek (2001) -1.627429 Bug's Life, A (1998) 0.538624
6 Shrek 2 (2004) -1.628034 Independence Day (a.k.a. ID4) (1996) 0.535614
7 Bug's Life, A (1998) -1.665477 Star Wars: Episode IV - A New Hope (1977) 0.535263
8 Ratatouille (2007) -1.672807 Aladdin (1992) 0.534045
9 Up (2009) -1.722887 Star Wars: Episode VI - Return of the Jedi (1983) 0.532928
First, fit the models and pick the best on the validation set:
python 2a_find_best.py
Then, get the test set performances:
python 2b_eval_on_test.py
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