Abstract
Selecting relevant travel attractions for a given user is a real and important problem from both a travellers’s and a travel supplier’s perspectives. Knowledge graphs have been used to conduct recommendations of music artists, movie and books. In this paper, we identify how knowledge graphs might be efficiently leveraged to recommend travel attractions. We improve two main drawbacks in existing systems where semantic information is exploited: fusion of multisource heterogeneous data and lack of spatial-temporal. Accordingly, we constructed a rich travel spatial-temporal knowledge graph from Baidupedia, Interactive Encyclopedia and Wikipedia. We proposed a methodnamed TSTKG4Rec to model the knowledge graph, it included two steps, attraction2Vec to model the feature attributes of attractions in the knowledge graph, and track2Vec to model the spatial-temporal semantics of the knowledge graph. Then, we obtained attraction vectors and user vectors fused with feature attributes of attractions and spatial-temporal semantics. At last we calculate the correlation between tourists and attractions with cosine similarity to give a list of recommendations. Our evaluation on real travel spatial-temporal knowledge graph showed that our approach improvement in terms of recall and MRR compared with the state-of-the-art approach.
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Acknowledgment
This work was partially supported by the National Natural Science Foundation of China under Grant U1501252 and Grant 61572146, and partially supported by the Natural Science Foundation of Guangxi Province under Grant 2016GXNSFDA380006 and Guangxi Innovation Driven Development Project (Science and Technology Major Project) AA17202024.
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Zhang, W., Gu, T., Sun, W., Phatpicha, Y., Chang, L., Bin, C. (2018). Travel Attractions Recommendation with Travel Spatial-Temporal Knowledge Graphs. In: Zhou, Q., Miao, Q., Wang, H., Xie, W., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 902. Springer, Singapore. https://doi.org/10.1007/978-981-13-2206-8_19
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DOI: https://doi.org/10.1007/978-981-13-2206-8_19
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