A survey on heterogeneous graph embedding: methods, techniques, applications and sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
When large language models meet personalization: Perspectives of challenges and opportunities
The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …
intelligence. With the unprecedented scale of training and model parameters, the capability …
A survey on knowledge graph-based recommender systems
To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …
applications, recommender systems have been developed to model users' preferences …
Explainable reasoning over knowledge graphs for recommendation
Incorporating knowledge graph into recommender systems has attracted increasing
attention in recent years. By exploring the interlinks within a knowledge graph, the …
attention in recent years. By exploring the interlinks within a knowledge graph, the …
Heterogeneous information network embedding for recommendation
Due to the flexibility in modelling data heterogeneity, heterogeneous information network
(HIN) has been adopted to characterize complex and heterogeneous auxiliary data in …
(HIN) has been adopted to characterize complex and heterogeneous auxiliary data in …
Leveraging meta-path based context for top-n recommendation with a neural co-attention model
Heterogeneous information network (HIN) has been widely adopted in recommender
systems due to its excellence in modeling complex context information. Although existing …
systems due to its excellence in modeling complex context information. Although existing …
Metapath-guided heterogeneous graph neural network for intent recommendation
With the prevalence of mobile e-commerce nowadays, a new type of recommendation
services, called intent recommendation, is widely used in many mobile e-commerce Apps …
services, called intent recommendation, is widely used in many mobile e-commerce Apps …
Recurrent knowledge graph embedding for effective recommendation
Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing
methods mainly rely on hand-engineered features from KGs (eg, meta paths), which …
methods mainly rely on hand-engineered features from KGs (eg, meta paths), which …
A survey of heterogeneous information network analysis
Most real systems consist of a large number of interacting, multi-typed components, while
most contemporary researches model them as homogeneous information networks, without …
most contemporary researches model them as homogeneous information networks, without …
Conet: Collaborative cross networks for cross-domain recommendation
The cross-domain recommendation technique is an effective way of alleviating the data
sparse issue in recommender systems by leveraging the knowledge from relevant domains …
sparse issue in recommender systems by leveraging the knowledge from relevant domains …