Multi-task learning has been widely applied in computational vision, natural language
processing and other fields, which has achieved well performance. In recent years, a lot of …

The main idea of multimodal recommendation is the rational utilization of the item's
multimodal information to improve the recommendation performance. Previous works …

To address the problem of data sparsity in recommendation systems, various studies have
used knowledge graphs as auxiliary information. These studies have employed multitask …

Introducing knowledge graphs into recommender systems effectively solves sparsity and
cold start problems. However, existing KG recommendation methods such as MKR mostly …

Over the past ten years, social networks (SN) have evolved into the primary infrastructure for
people's everyday activities. Trust classification in social networks involves evaluating the …

Recommendation systems typically rely on users' historical behavior to infer their
preferences. However, when new entries emerge, the system cannot make accurate …

The construction of large open knowledge bases (OKBs) is integral to many knowledge-
driven applications on the world wide web such as web search. However, noun phrases in …

To tackle the challenges of cold start and data sparsity in recommendation systems, an
increasing number of researchers are integrating item features, resulting in the emergence …

The use of Artificial Intelligence (AI) systems and large datasets to make decisions on behalf
of users is becoming more common in critical areas such as healthcare, agriculture, and law …