A neural multi-context modeling framework for personalized attraction recommendation

• Published in: Multimedia tools and applications 2020-06, Vol.79 (21-22), p.14951-14979
• Main Authors: Bin, Chenzhong, Gu, Tianlong, Jia, Zhonghao, Zhu, Guimin, Xiao, Cihan
• Format: Article
• Language: English
• Subjects: Attraction; Computer Communication Networks; Computer Science; Context; Data Structures and Information Theory; Knowledge bases (artificial intelligence); Modelling; Multimedia Information Systems; Neural networks; Representations; Special Purpose and Application-Based Systems; Tourism
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-019-08554-5

In attraction recommendation scenarios, how to model multifaceted tourism contexts so as to accurately learn tourist preferences and attraction tourism features is a keystone of generating personalized recommendations. However, most of existing works generally focused on modeling spatiotemporal contexts of historical travel trajectories to learn tourists’ preferences, while neglected rich heterogeneous tourism side information, i.e., personal tourism constraints of tourists and tourism attributes of attractions. To this end, we propose a Neural Multi-context Modeling Framework (NMMF) to learn tourism feature representations of tourists and attractions by modeling multiple tourism contexts. Initially, we leverage a travel knowledge graph and massive original travelogues to construct the tourism attribute context of attractions and the travel trajectory context of tourists. Then, we design two context embedding models, named TKG2vec and Traj2vec, to model two kinds of context respectively. Both models learn feature vectors of tourist and attraction in contexts by elaborating neural networks to project each tourist and attraction into a uniform latent feature space. Finally, our framework integrates feature vectors derived from two models to acquire complete feature representations of tourists and attractions, and recommends personalized attractions by calculating the similarity between tourist and candidate attractions in the latent space. Experimental results on a real-world tourism dataset demonstrate our framework outperforms state-of-the-art methods in two personalized attraction recommendation tasks.