Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
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by
Jianxun Lian, Iyad Batal, Zheng Liu, Akshay Soni, Eun Yong Kang, Yajun Wang, Xing Xie
2021
Abstract
Precise user modeling is critical for online personalized recommendation
services. Generally, users' interests are diverse and are not limited to a
single aspect, which is particularly evident when their behaviors are observed
for a longer time. For example, a user may demonstrate interests in cats/dogs,
dancing and food & delights when browsing short videos on Tik Tok; the same
user may show interests in real estate and women's wear in her web browsing
behaviors. Traditional models tend to encode a user's behaviors into a single
embedding vector, which do not have enough capacity to effectively capture her
diverse interests.
This paper proposes a Sequential User Matrix (SUM) to accurately and
efficiently capture users' diverse interests. SUM models user behavior with a
multi-channel network, with each channel representing a different aspect of the
user's interests. User states in different channels are updated by an
erase-and-add paradigm with interest- and instance-level attention. We
further propose a local proximity debuff component and a highway connection
component to make the model more robust and accurate. SUM can be maintained and
updated incrementally, making it feasible to be deployed for large-scale online
serving. We conduct extensive experiments on two datasets. Results demonstrate
that SUM consistently outperforms state-of-the-art baselines.
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