Abstract
The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content and contextual information that may influence the recommendation accuracy, especially tourist attractions’ visual content due to redundant and noisy geotagged photos; therefore, we propose a tourist attraction recommendation model for Flickr-geotagged photos which fuses spatial, temporal, and visual embeddings (STVE). After spatial clustering and extracting visual embeddings of tourist attractions’ representative images, the spatial and temporal embeddings are modeled with the Word2Vec negative sampling strategy, and the visual embeddings are fused with Matrix Factorization and Bayesian Personalized Ranking. The combination of these two parts comprises our proposed STVE model. The experimental results demonstrate that our STVE model outperforms other baseline models. We also analyzed the parameter sensitivity and component performance to prove the performance superiority of our model.
Highlights
With the advent of the “Web 3.0” era [1,2], the Internet users’ role has transformed from mere information receivers to producers and interactors of information
Given the collaborative filtering (CF)-based models’ cold-start problems and the content-based models’ low accuracy problems, we propose a hybrid recommendation model for tourist attractions that fuses spatial, temporal, and visual embeddings (STVE)
Bayesian Personalized Ranking-Matrix Factorization (BPR-MF): BPR-MF is a simple “user-item” matrix factorization method optimized with Bayesian Personalized Ranking
Summary
With the advent of the “Web 3.0” era [1,2], the Internet users’ role has transformed from mere information receivers to producers and interactors of information. Many studies on tourist attraction recommendation systems have emerged, which aims to meet tourists’ increasing demand for intelligent and personalized tourism and solve the problem of tourist information overload [8]. The machine learning field’s embedding models have gradually emerged and developed in the research of recommendation algorithms Using such a simple and efficient method to fuse content and contextual information in tourist attraction recommendations means that they can learn from each other and improve the recommendation accuracy. We propose a tourist attraction recommendation model fusing spatial, temporal, and visual embeddings (STVE) for geotagged photos. Given the CF-based models’ cold-start problems and the content-based models’ low accuracy problems, we propose a hybrid recommendation model for tourist attractions that fuses spatial, temporal, and visual embeddings (STVE).
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