Getting to Know Your Neighbors (KYN). Explaining Item Similarity in Nearest Neighbors Collaborative Filtering Recommendations
Joanna Misztal-Radecka, Bipin Indurkhya · 2020
The popular neighborhood-based Collaborative Filtering recommendation techniques are mostly characterized as black-box systems in which resulting outputs are not easy to interpret. In this work, our goal is to provide human-interpretable explanations of item-based collaborative filtering recommendations and to understand the underlying data distribution. We propose the Know Your Neighbors (KYN) algorithm - a novel model-agnostic approach to explaining similarity-based CF recommendations on both local and global levels. In this approach, a post-hoc explainer model is applied to reveal the most important descriptive features of items that explain the neighborhood function for two popular collaborative algorithms. Our algorithm is evaluated on two public recommendation datasets. The descriptions generated for both the datasets are consistent with our intuitive understanding of user behaviors, but they also reveal that each representation may be susceptible to different types of biases in the dataset.