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

Anonymous

Paper Link: https://openreview.net/forum?id=r_2z0-tufhS

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

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.

Presentation Mode: This paper will be presented virtually

Virtual Presentation Timezone: UTC-8

Copyright Consent Signature (type Name Or NA If Not Transferrable): Rui Feng

Copyright Consent Name And Address: Rui Feng, North Ave NW, Atlanta, GA 30332