This notebook provides an implementation in Python 3 of the algorithm outlined in the paper "Negative Interactions for Improved Collaborative Filtering: Don’t go Deeper, go Higher" published at the 15th ACM Conference on Recommender Systems(RecSys 2021), Amsterdam, Netherlands.
The results of Table 1 in this paper can be reproduced in the following three steps:
- Step 1: Pre-processing the data (as in this publicly available code)
- Step 2: Loading the pre-processed data, and defining the evaluation-functions (as in this publicly available code)
- Step 3: Learning and Evaluating the higher-order model in this paper.
We use the same code for pre-processing the data and evaluating the model as was made publicly available in this code), which accompanies the paper "Variational autoencoders for collaborative filtering" by Dawen Liang et al. at The Web Conference 2018. While their code for the Movielens-20M data-set was made publicly available, the code for pre-processing the other two data-sets can easily be obtained by modifying their code as described in their paper.