一个新的基于会话的推系统,使用囊图神经网络
Driss El Alaoui1, Jamal Riffi1, Abdelouahed Sabri1
1LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, 1796 Fez-Atlas, Fez, 30000, Morocco.
概括
本研究介绍了基于会话的推 (CapsGSR) 的囊图形SAGE,这是一种通过更好地理解复杂的用户旅程来改善产品发现的新模型. CapsGSR提高了推准确度,从而带来了更好的客户体验.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 基于会话的推系统 (SBRS) 对电子商务至关重要,它在不依赖历史数据的情况下增强了用户体验和销售.
- 当前的SBRS模型难以捕捉会话中的复杂项目转换,并忽略了跨多个用户会话的更广泛模式.
研究的目的:
- 提出一种新的SBRS模型,即基于会话推的囊图形信息 (CapsGSR),旨在克服现有算法的局限性.
- 为了增强对象的表示和用户会话中的复杂过渡的理解.
主要方法:
- 拟议的CapsGSR模型将GraphSAGE的感应学习与Capsules网络的多视角抽象集成在一起.
- 它生成多个节点表示,捕捉复杂的项目过渡动态和可概括的模式.
主要成果:
- 在基准数据集上,CapsGSR显著优于传统和当前SBRS基线模型.
- 证明的改善包括8.44%的Hit Rate@20 (HR@20) 的增加和4.66%的Mean Reciprocal Rank@20 (MRR@20) 的增加.
结论:
- CapsGSR有效地解决了以前的SBRS的局限性,提供了更精确和更相关的项目建议.
- 该模型能够捕捉复杂的过渡和一般模式,从而在动态推场景中获得更高的性能.
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