为 Grab 使用基础模型

Artificial intelligence (AI) is central to Grab’s mission of delivering valuable, personalised experiences to millions of users across Southeast Asia. Achieving this requires a deep understanding of individual preferences, such as their favorite foods, relevant advertisements, spending habits, and more. This personalisation is driven by recommender models, which depend heavily on high-quality representations of the user.

人工智能（AI）是 Grab 使命的核心，旨在为东南亚数百万用户提供有价值且个性化的体验。实现这一目标需要深入理解个人偏好，例如他们喜爱的食物、相关广告、消费习惯等。这种个性化由推荐模型驱动，而推荐模型又高度依赖高质量的用户表示。

Traditionally, these models have relied on hundreds to thousands of manually engineered features. Examples include the types of food ordered in the past week, the frequency of rides taken, or the average spending per transaction. However, these features were often highly specific to individual tasks, siloed within teams, and required substantial manual effort to create. Furthermore, they struggled to effectively capture time-series data, such as the sequence of user interactions with the app.

传统上，这些模型依赖数百到数千个手工设计的特征。例如，过去一周订购的食物类型、打车频率或每笔交易的平均消费额。然而，这些特征往往高度针对单个任务，被团队孤立维护，且需要大量人工来创建。此外，它们难以有效捕捉时间序列数据，例如用户与 App 交互的顺序。

With advancements in learning from tabular and sequential data, Grab has developed a foundation model that addresses these limitations. By simultaneously learning from user interactions (clickstream data) and tabular data (e.g. transaction data), the model generates user embeddings that capture app behavior in a more holistic and generalised manner. These embeddings, represented as numerical values, serve as input features for downstream recommender models, enabling higher levels of personalisation and improved performance. Unlike manually engineered features, they generalise effectively across a wide range of tasks, including advertisement optimisation, dual app prediction, fraud detection, and churn probability, among others.

随着从表格和序列数据中学习的技术进步，Grab 开发了一种基础模型来解决这些限制。通过同时学习用户交互（点击流数据）和表格数据（例如交易数据），该模型生成能够更全面、更泛化地捕捉应用行为的用户嵌入。这些以数值形式表示的嵌入作为下游推荐模型的输入特征，能够实现更高水平的个性化并提升性能。与...