用大规模记忆块存用户历史行为，提升推荐长期准确性
Large Memory Network for Recommendation
- 构建大容量记忆块压缩存储用户行为序列
- 在抖音电商搜索中线上测试，显著提升推荐效果
- 适合需要长期用户兴趣建模的推荐系统场景
在推荐系统中建模用户行为序列对理解用户偏好至关重要，有助于提升个性化推荐精度、增强用户留存与商业价值。然而现有方法面临双重挑战：空间维度上难以捕捉相似用户的共同兴趣以实现意图泛化；时间维度上因输入序列长度固定，易遗忘长期兴趣。本文提出大型记忆网络（LMN），通过压缩并存储用户历史行为信息至大规模记忆块，结合高效的在线部署策略，使记忆块可轻松扩展至百万级规模。在抖音电商搜索（ECS）上开展大量离线对比实验、记忆扩展实验及线上A/B测试，验证了LMN的优越性能。目前，该模型已在抖音电商搜索中全面部署，每日服务数百万用户。
原文摘要 · Abstract (English)
Modeling user behavior sequences in recommender systems is essential for understanding user preferences over time, enabling personalized and accurate recommendations for improving user retention and enhancing business values. Despite its significance, there are two challenges for current sequential modeling approaches. From the spatial dimension, it is difficult to mutually perceive similar users' interests for a generalized intention understanding; from the temporal dimension, current methods are generally prone to forgetting long-term interests due to the fixed-length input sequence. In this paper, we present Large Memory Network (LMN), providing a novel idea by compressing and storing user history behavior information in a large-scale memory block. With the elaborated online deployment strategy, the memory block can be easily scaled up to million-scale in the industry. Extensive offline comparison experiments, memory scaling up experiments, and online A/B test on Douyin E-Commerce Search (ECS) are performed, validating the superior performance of LMN. Currently, LMN has been fully deployed in Douyin ECS, serving millions of users each day.
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