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arXiv · 2204.04303

CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data

Rui Feng·Chen Luo·Qingyu Yin·Bing Yin·Tuo Zhao·Chao Zhang
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Abstract

User sessions empower many search and recommendation tasks on a daily basis. Such session data are semi-structured, which encode heterogeneous relations between queries and products, and each item is described by the unstructured text. Despite recent advances in self-supervised learning for text or graphs, there lack of self-supervised learning models that can effectively capture both intra-item semantics and inter-item interactions for semi-structured sessions. To fill this gap, we propose CERES, a graph-based transformer model for semi-structured session data. CERES learns representations that capture both inter- and intra-item semantics with (1) a graph-conditioned masked language pretraining task that jointly learns from item text and item-item relations; and (2) a graph-conditioned transformer architecture that propagates inter-item contexts to item-level representations. We pretrained CERES using ~468 million Amazon sessions and find that CERES outperforms strong pretraining baselines by up to 9% in three session search and entity linking tasks.

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Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao, Chao Zhang. 2022-04-08. CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data. https://arxiv.org/abs/2204.04303

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