Name
Title | ||
|---|---|---|
Reduce unrelated Knowledge through Attribute Collaborative signal for knowledge graph recommendation |
Abstract | ||
|---|---|---|
Knowledge graph (KG), as an auxiliary information, plays an important role in the recommendation system, which effectively solves the sparsity and cold start problems of collaborative filtering algorithms. The recommendation algorithm that introduces the propagation mechanism on the KG has been a great success, it enriches the representation of users and items by aggregating multi-hop neighbors. However, the existing KG-based propagation recommendation algorithm aggregating all entity information cannot guarantee the improvement of recommendation results, because entity information in KG is not all helpful to recommend appropriate items to users. Indiscriminately aggregating the entity information in the neighborhood allows the learned embedding representation to be influenced by its unrelated entities. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.eswa.2022.117078 | Expert Systems with Applications |
Keywords | DocType | Volume |
Recommender system,Knowledge graph,Collaborative signal,Attention mechanism | Journal | 201 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fulan Qian | 1 | 6 | 4.49 |
| Yuhui Zhu | 2 | 0 | 0.34 |
| Hai Chen | 3 | 0 | 0.34 |
| Jie Chen | 4 | 91 | 38.15 |
| Shu Zhao | 5 | 0 | 0.34 |
| Yanping Zhang | 6 | 7 | 3.81 |