In recent years, knowledge graph networks for recommendation have attracted extensive
attention, since these methods can capture structured information by linking items with their …

To address the sparsity and cold-start problem of collaborative filtering, researchers usually
make use of side information, such as social networks or item attributes, to improve the …

Recommender algorithms combining knowledge graph and graph convolutional network
are becoming more and more popular recently. Specifically, attributes describing the items …

Although knowledge graph has shown their effectiveness in mitigating data sparsity in many
recommendation tasks, they remain underutilized in context-aware recommender systems …

This paper studies graph-based recommendation, where an interaction graph is built from
historical responses and is leveraged to alleviate data sparsity and cold start problems. We …

Since it can effectively address the problem of sparsity and cold start of collaborative
filtering, knowledge graph (KG) is widely studied and employed as side information in the …

Recently, much attention has been paid to the usage of knowledge graph within the context
of recommender systems to alleviate the data sparsity and cold-start problems. However …

The growing number of publications in the field of artificial intelligence highlights the need
for researchers to enhance their efficiency in searching for relevant articles. Most paper …

The explosive growth of machine learning (ML) methods is overloading users with choices
for learning tasks. Method recommendation aims to alleviate this problem by selecting the …

Abstract Knowledge Graph Embedding (KGE) aims to retain the intrinsic structural
information of knowledge graphs (KGs) via representation learning, which is critical for …