Abstract
Personalized travel mashups aim to assist users in making decision i.e what places to visit. To facilitate human decisions with credible suggestions, these systems should have the ability to generate corresponding explanations while making recommendations. Knowledge graphs (KG), which contain rich and comprehensive information among items are widely used to enable this. By reasoning over a KG in a node-by-node manner, the connectivity between items can be discovered as paths that serve as an explanation to enhance the interpretability of recommendations. However, existing methods failed to utilize the information of collective-level POI sequences. The individual-level cannot represent more holistic semantic features and cannot express complete transition patterns. To this end, we propose knowledge-aware approach for explainable travel mashup that joints the multi-granularity representation and the attention mechanism to capture the sequential dependencies at collective-level POI on different granularities. Specifically, we encode a diversity of semantic relations and connectivity patterns into a travel knowledge graph. Then, we employ a recurrent network architecture to exploit the semantics of paths entities pair, which are fused into explainable recommendation using attentive graph. Extensive validation on a real-world datasets shows the effectiveness of the proposed approach.
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Boulakbech, M., Messai, N., Sam, Y., Devogele, T. (2022). Attentive Knowledge-Aware Path Network for Explainable Travel Mashup. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_37
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