Large Memory Network for Recommendation
Journal:
arXiv
Published Date:
Feb 8, 2025
Abstract
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.
