Travel Attractions Recommendation with Knowledge Graphs.

Selecting relevant travel attractions for a given user is a real and important problem from both a traveller's and a travel supplier's perspectives. Knowledge graphs have been used to conduct recommendations of music artists, movies 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: semantic poorness and city-agnostic user profiling strategy. Accordingly, we constructed a rich world scale travel knowledge graph from existing large knowledge graphs namely Geonames, DBpedia and Wikidata. The underlying ontology contains more than 1200 classes to describe attractions. We applied a city-dependent user profiling strategy that makes use of the fine semantics encoded in the constructed graph. Our evaluation on YFCC100M dataset showed that our approach achieves a 5.3﾿% improvement in terms of F1-score, a 4.3﾿% improvement in terms of nDCG compared with the state-of-the-art approach.