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About this paper
Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen
Google, Inc. researchers developed a variational autoencoder-based framework to model latent user intent for sequential recommender systems, implicitly inferring user goals from behavior signals. This approach led to a 0.07% improvement in overall user enjoyment and a 0.10% increase in interaction diversity in large-scale live experiments.
Key insights
- The continuous latent intent space, quantifiable through KL-divergence, can effectively capture significant shifts or "surprise" in user behavior, such as exploring new items or topics.
- Implicitly modeling user intent using VAEs can enhance recommendation quality and long-term user experience without requiring laborious manual intent labeling.
- Techniques like proper parameter initialization and soft-clipping log-variance are crucial for achieving training stability in probabilistic models when scaled to industrial-level data.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.