Zero-shot Item-based Recommendation via Multi-task Product Knowledge
  Graph Pre-Training
Ziwei Fan Zhiwei Liu Shelby Heinecke Jianguo Zhang Huan Wang Caiming Xiong Philip S. Yu
Existing recommender systems face difficulties with zero-shot items, i.e.
items that have no historical interactions with users during the training
stage. Though recent works extract universal item representation via
pre-trained language models (PLMs), they ignore the crucial item relationships.
This paper presents a novel paradigm for the Zero-Shot Item-based
Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph
(PKG) to refine the item features from PLMs. We identify three challenges for
pre-training PKG, which are multi-type relations in PKG, semantic divergence
between item generic information and relations and domain discrepancy from PKG
to downstream ZSIR task. We address the challenges by proposing four
pre-training tasks and novel task-oriented adaptation (ToA) layers. Moreover,
this paper discusses how to fine-tune the model on new recommendation task such
that the ToA layers are adapted to ZSIR task. Comprehensive experiments on 18
markets dataset are conducted to verify the effectiveness of the proposed model
in both knowledge prediction and ZSIR task.