STAN推荐系统无需复杂解释工具，直接展示相似用户和关键时间步。
Explaining the (Not So) Obvious: Simple and Fast Explanation of STAN, a Next Point of Interest Recommendation System
- 基于协同过滤与序列预测，直接输出相似用户和重要时间点。
- 解释结果能有效定位推荐错误，辅助调试模型输出。
- 适合需要可解释性推荐的场景，如位置服务、导航应用。
近年来大量研究致力于解释机器学习系统，但部分方法本身具备可解释性，无需复杂解释技术即可理解输出。解释应贴合具体问题背景：在依赖协同过滤的推荐系统中，解释应说明哪些用户与当前用户相似；若基于序列预测，则需指出哪些输入时间步影响最大。本文以基于协同过滤与序列预测的下一站兴趣点推荐系统STAN（Spatio-Temporal Attention Network for Next Location Recommendation）为例，验证了该理念。实验表明，其解释结果有助于识别推荐异常，实现模型输出的快速调试。
原文摘要 · Abstract (English)
A lot of effort in recent years have been expended to explain machine learning systems. However, some machine learning methods are inherently explainable, and thus are not completely black box. This enables the developers to make sense of the output without a developing a complex and expensive explainability technique. Besides that, explainability should be tailored to suit the context of the problem. In a recommendation system which relies on collaborative filtering, the recommendation is based on the behaviors of similar users, therefore the explanation should tell which other users are similar to the current user. Similarly, if the recommendation system is based on sequence prediction, the explanation should also tell which input timesteps are the most influential. We demonstrate this philosophy/paradigm in STAN (Spatio-Temporal Attention Network for Next Location Recommendation), a next Point of Interest recommendation system based on collaborative filtering and sequence prediction. We also show that the explanation helps to "debug" the output.
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