Benchmark compares explanation methods for sequence recommendation models
A Systematic Benchmark of Explainable Methods for Temporal Attribution in Sequential Recommendation Systems
Machine Learning
Summary
Personalized recommendation systems suggest what you might like next by looking at your past actions. The models behind them are complex, making it hard to understand why certain suggestions are made. The authors compare different ways to explain these models’ decisions, checking which methods best reveal which past actions influenced the recommendation. They find that some gradient-based techniques work well, while simply using attention scores can be misleading. This helps make recommendation systems more transparent and trustworthy.
What this means in practice
- •For software engineers: Improve debugging by using faithful explanation methods to understand why recommendation systems suggest specific items.
- •For product managers: Design clearer user interfaces that show trustworthy reasons behind recommended items using reliable attribution methods.
Authors
Akash Pandey, Kanisha Shah, Addrish Roy, Dwipam Katariya, Hongyangyang Shi, Amanda Ding, Kalanand Mishra, Pranab Mohanty
Abstract
Sequential RecSys are central to modern personalization, exploiting user's historical interaction sequences to drive next-step decisions. Deep learning models, particularly CNN and Transformer-based architectures, have proven highly effective at capturing temporal dependencies in these histories. For transparency and trust, understanding which past interactions drive a given recommendation is increasingly important --- both for developers auditing model behavior and for users seeking a rationale. However, the non-linearities that give these models their predictive power also render them black boxes, making it difficult to attribute decisions to specific interactions. While gradient-based, perturbation-based, and attention-based explainability methods exist, a systematic benchmark of their faithfulness for sequential recommendation is missing. We address this gap by introducing a dual-model masking metric in which one model supplies per-timestep attribution scores and a separately trained, masking-robust probe measures the resulting change in predicted probability. Using this metric, we benchmark ten XAI methods across CNN, Transformer, SASRec, and BERT4Rec backbones on KuaiRand and MovieLens, complemented by analyses of temporal attribution patterns, item popularity confounding, and robustness to input corruption. Our key findings are: (1) gradient-based methods, particularly GradientSHAP and Integrated Gradients, yield the most faithful and robust attributions; (2) raw attention weights are unreliable, but gradient-weighted attention restores faithfulness on shorter sequences, with degradation on longer horizons as softmax attention probabilities converge toward uniform importance scores, diminishing the method's ability to identify informative interactions; and (3) temporal attribution patterns in faithful methods reflect genuine task structure rather than recency or popularity bias.