Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and
industry. Conventional KG reasoning based on symbolic logic is deterministic, with …

E-commerce pre-sales dialogue aims to understand and elicit user needs and preferences
for the items they are seeking so as to provide appropriate recommendations …

Medication recommendation systems have gained significant attention in healthcare as a
means of providing tailored and effective drug combinations based on patients' clinical …

In this paper, we address multi-modal pretraining of product data in the field of E-commerce.
Current multi-modal pretraining methods proposed for image and text modalities lack …

Augmenting conversational recommendation systems (CRS) with prior knowledge is crucial
for learning user preferences and understanding contextual semantics. Existing methods are …

Rule mining is an effective approach for reasoning over knowledge graph (KG). Existing
works mainly concentrate on mining rules. However, there might be several rules that could …

This paper aims to establish a generic multi-modal foundation model that has the scalable
capability to massive downstream applications in E-commerce. Recently, large-scale vision …

While Graph Neural Networks (GNNs) have demonstrated remarkable achievements in
knowledge graph reasoning, their computational efficiency on conventional computing …

The diversified recommendation of recommender systems enriches user experiences by
diversifying recommendation lists. However, the conventional post-processing strategy …