| Autor: |
Yuqi ZHANG, Xiaowen HUANG, Jitao SANG |
| Jazyk: |
čínština |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
大数据, Vol 8, Pp 88-105 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2096-0271 |
| DOI: |
10.11959/j.issn.2096-0271.2022033 |
| Popis: |
The recommendation system is an important means to solve the problem of information overload in social media.To solve the problem that traditional recommendation systems cannot optimize the longterm user experience, researchers have proposed the interactive recommendation system and tried to use deep reinforcement learning to optimize the strategy of recommendation.However, the reinforcement recommendation algorithm faces problems such as sparse feedback, learning from zero which damages the user experience, and large item space.To solve the above problems, an improved interactive reinforcement recommendation model KGP-DQN was proposed.The model constructed a behavioral knowledge graph representation module, which combines user historical behavior and knowledge graph to solve the problem of sparse feedback.The model constructed a strategy initialization module to provide an initialization strategy for the reinforcement recommendation system based on user historical behaviors to solve the problem of learning from zero.The model constructed the candidate select module which creates candidates by dynamic clustering based on the item representation on the behavioral knowledge graph to solve the problem of large action space.The experiments were conducted on three real-world datasets.The experimental results show that the KGP-DQN method can quickly and effectively train the reinforcement recommendation system and its recommendation accuracy on three datasets is more than 80%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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