Towards travel recommendation interpretability: Disentangling tourist decision-making process via knowledge graph
文献类型：期刊论文
| 作者 | Gao, Jialiang3; Peng, Peng3; Lu, Feng2,3,5; Claramunt, Christophe1; Xu, Yang3 |
| 刊名 | INFORMATION PROCESSING & MANAGEMENT
 |
| 出版日期 | 2023-07-01 |
| 卷号 | 60期号:4页码:103369 |
| 关键词 | Decision -making process Recommendation system Interpretability Knowledge graph Disentangled learning Tourism management |
| ISSN号 | 0306-4573 |
| DOI | 10.1016/j.ipm.2023.103369 |
| 文献子类 | Article |
| 英文摘要 | Understanding tourists' decision-making processes, in which many factors ranging from functional attributes to geographical configurations are highly intertwined, has long been a crux for tourism management. Existing studies are typically based on manual surveys that extract the intricate psychological or behavioural mechanisms, but the huge expense of the required samplings limits the generalization and comprehensiveness of the findings. This study proposes a novel explainable recommendation method-Knowledge-Graph-aware Disentangled AutoEncoder (KGDAE)-to automatically unravel the tourists' decision processes from massive historical behaviour data. Based on the constructed tourism-KG that integrates multidimensional factors into 23 types of entities corresponding to 37 semantic and geographic relationships, KGDAE realizes a macro-micro supervised disentangled learning for the interaction of multiple determinants. Macroscopically, the hierarchical attention mechanisms are designed to distinguish the dominance of either functional or geographical factors, and capture the effect of the residential environment; microscopically, the preference-propagation-based technique is introduced to infer the fine-grained characteristics and relations of tourist interests on the tourism-KG. Extensive experiments show that KGDAE can effectively restore tourists' decision processes according to two empirical studies while boosting the recommendation performance compared to multiple state-of-the-art methods with an increase of 1 similar to 19%. Furthermore, the advantaged interpretability also guarantees the robustness of sparse recommendation scenario to achieve the lowest degradation at 7.8%. |
| 学科主题 | Computer Science ; Information Science & Library Science |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/193482]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Naval Acad Res Inst, Brest, France 5.Fuzhou Univ, Acad Digital China, Fuzhou 350002, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Jialiang,Peng, Peng,Lu, Feng,et al. Towards travel recommendation interpretability: Disentangling tourist decision-making process via knowledge graph[J]. INFORMATION PROCESSING & MANAGEMENT,2023,60(4):103369. |
| APA | Gao, Jialiang,Peng, Peng,Lu, Feng,Claramunt, Christophe,&Xu, Yang.(2023).Towards travel recommendation interpretability: Disentangling tourist decision-making process via knowledge graph.INFORMATION PROCESSING & MANAGEMENT,60(4),103369. |
| MLA | Gao, Jialiang,et al."Towards travel recommendation interpretability: Disentangling tourist decision-making process via knowledge graph".INFORMATION PROCESSING & MANAGEMENT 60.4(2023):103369. |
入库方式： OAI收割
来源：地理科学与资源研究所
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