论文标题
部分可观测时空混沌系统的无模型预测
PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions
论文作者
论文摘要
新兴的元和多重景观是朝着更普遍使用本来普遍存在的在线市场迈出的又一步。在这样的市场中，推荐系统通过向用户提供感兴趣的物品来扮演关键角色，从而缩小了庞大的搜索空间，该搜索空间包括成千上万的产品。推荐系统通常旨在学习常见的用户行为并依靠它们进行推断。这种方法虽然有效，但却忽略了彼此区分人类的微妙特质。为了关注这一观察结果，我们提出了一个依赖共同模式以及个人行为来量身定制每个人的建议的建筑。在受控环境下的模拟表明，我们提出的模型学习了可解释的个性化用户行为。我们对Nielsen消费面板数据集的经验结果表明，与最先进的ART相比，所提出的方法的性能提高了27.9％。
The emerging meta- and multi-verse landscape is yet another step towards the more prevalent use of already ubiquitous online markets. In such markets, recommender systems play critical roles by offering items of interest to the users, thereby narrowing down a vast search space that comprises hundreds of thousands of products. Recommender systems are usually designed to learn common user behaviors and rely on them for inference. This approach, while effective, is oblivious to subtle idiosyncrasies that differentiate humans from each other. Focusing on this observation, we propose an architecture that relies on common patterns as well as individual behaviors to tailor its recommendations for each person. Simulations under a controlled environment show that our proposed model learns interpretable personalized user behaviors. Our empirical results on Nielsen Consumer Panel dataset indicate that the proposed approach achieves up to 27.9% performance improvement compared to the state-of-the-art.
