Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training
This addresses the challenge of recommending items with no historical interactions, which is a common issue in recommender systems, though it is incremental in improving existing methods.
The paper tackles the problem of zero-shot item-based recommendation by pre-training on a product knowledge graph to refine item features from language models, achieving state-of-the-art results in knowledge prediction and recommendation tasks across 18 markets.
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.