Linear autoencoders reveal interpretable topics from recommendations
Discovering Topics from Linear Autoencoders
Information RetrievalMachine Learning
Summary
Finding hidden topics can help explain why certain recommendations are made in systems like movie or product suggestions. The authors show that a special kind of recommender system called a linear autoencoder, which is easy to train, actually contains these topics in its internal settings. They also introduce ways to fix biases caused by popular items or very active users to make the topics clearer. Their method performs better at finding meaningful topics than existing topic models and still recommends well.
What this means in practice
- •For recommender system engineers: Extract interpretable topics from trained linear recommenders to improve explanation and user trust without extra training steps.
- •For content platform data scientists: Reduce influence of highly popular items and hyper-active users on topic discovery to get more balanced content insights.
Authors
Jutiyakhun Srisoparp, Donlapark Ponnoprat
Abstract
Linear recommenders and topic models are similar in that they implicitly learn from co-occurrences of items. However, there has been little study of how these two model families are related. In this work, we show that, under a latent-factor model of the user-item interactions, the latent topics can be discovered from the weight matrix of a closed-form linear autoencoder. These topics provide a basis for interpreting the recommendations, and can be obtained directly from the trained recommender without additional training. Building on our analysis, we further propose novel item and user normalizations that prevent popular items and very active users from dominating the topics. Our experiments on six public datasets demonstrate that our method achieves significantly higher topic quality than every dedicated topic model and matches or exceeds the best linear autoencoder on all six datasets, while its recommendation performance is competitive with the best tuned recommender.