End-to-end Learnable Clustering for Intent Learning in Recommendation
- URL: http://arxiv.org/abs/2401.05975v3
- Date: Fri, 2 Feb 2024 17:30:51 GMT
- Title: End-to-end Learnable Clustering for Intent Learning in Recommendation
- Authors: Yue Liu, Shihao Zhu, Jun Xia, Yingwei Ma, Jian Ma, Wenliang Zhong,
Xinwang Liu, Guannan Zhang, Kejun Zhang
- Abstract summary: We propose a novel intent learning method termed underlineELCRec.
It unifies behavior representation learning into an underlineEnd-to-end underlineLearnable underlineClustering framework.
We deploy this method on the industrial recommendation system with 130 million page views and achieve promising results.
- Score: 61.29127008174193
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Intent learning, which aims to learn users' intents for user understanding
and item recommendation, has become a hot research spot in recent years.
However, the existing methods suffer from complex and cumbersome alternating
optimization, limiting the performance and scalability. To this end, we propose
a novel intent learning method termed \underline{ELCRec}, by unifying behavior
representation learning into an \underline{E}nd-to-end \underline{L}earnable
\underline{C}lustering framework, for effective and efficient
\underline{Rec}ommendation. Concretely, we encode users' behavior sequences and
initialize the cluster centers (latent intents) as learnable neurons. Then, we
design a novel learnable clustering module to separate different cluster
centers, thus decoupling users' complex intents. Meanwhile, it guides the
network to learn intents from behaviors by forcing behavior embeddings close to
cluster centers. This allows simultaneous optimization of recommendation and
clustering via mini-batch data. Moreover, we propose intent-assisted
contrastive learning by using cluster centers as self-supervision signals,
further enhancing mutual promotion. Both experimental results and theoretical
analyses demonstrate the superiority of ELCRec from six perspectives. Compared
to the runner-up, ELCRec improves NDCG@5 by 8.9\% and reduces computational
costs by 22.5\% on Beauty dataset. Furthermore, due to the scalability and
universal applicability, we deploy this method on the industrial recommendation
system with 130 million page views and achieve promising results.
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