序列学习：个性化广告推荐的范式转变

AI plays a fundamental role in creating valuable connections between people and advertisers within Meta’s family of apps. Meta’s ad recommendation engine, powered by deep learning recommendation models (DLRMs), has been instrumental in delivering personalized ads to people. Key to this success was incorporating thousands of human-engineered signals or features in the DLRM-based recommendation system.

AI在Meta应用家族中创建人与广告商之间的有价值连接方面发挥了基础性作用。Meta的广告推荐引擎由深度学习推荐模型（DLRM）提供支持，在向人们提供个性化广告方面发挥了重要作用。成功的关键在于在基于DLRM的推荐系统中纳入了数千个人工设计的信号或特征。

Despite training on vast amounts of data, there are limitations to current DLRM-based ads recommendations with manual feature engineering due to the inability of DLRMs to leverage sequential information from people’s experience data. To better capture the experiential behavior, the ads recommendation models have undergone foundational transformations along two dimensions:

尽管在大量数据上进行训练，但由于DLRM无法利用人们体验数据中的顺序信息，当前基于DLRM的广告推荐在手动特征工程方面存在局限性。为了更好地捕捉体验行为，广告推荐模型在两个维度上进行了基础性转变：

Event-based learning: learning representations directly from a person’s engagement and conversion events rather than traditional human-engineered features.
基于事件的学习：直接从一个人的参与和转化事件中学习表示，而不是传统的人为设计特征。
Learning from sequences: developing new sequence learning architectures to replace traditional DLRM neural network architectures.
从序列中学习：开发新的序列学习架构以取代传统的DLRM神经网络架构。

By incorporating these advancements from the fields of natural language understanding and computer vision, Meta’s next-generation ads recommendation engine addresses the limitations of traditional DLRMs, resulting in more relevant ads for people, higher value for advertisers, and better infrastructure efficiency.

通过结合自然语言理解和计算机视觉领域的这些进步，Meta的下一代广告推荐引擎解决了传统DLRM的局限性，从而为人们提供了更相关的广告，为广告商带来了更高的价值，并提高了基础设施效率。

These innovations have enabled our ads system to develop a deeper understanding of people’s behavior before and after converting on an ad, enabling us to infer the next set of relevant ads. Since launch, the new ads recommendation system has improved ads prediction accura...