基于注意力的下一城市出行推荐神经重排序方法
Attention-based neural re-ranking approach for next city in trip recommendations
摘要：
本文描述了一种解决旅游预订系统下一个目的地城市推荐问题的方法。我们提出了一个两阶段的方法：一个启发式的候选人选择方法和一个注意力神经网络模型的候选人重新排名。我们的方法的灵感来自列表学习排名方法和自然语言处理的最新发展，特别是Transformer架构。我们使用这种方法来解决Booking.com推荐挑战我们的团队使用这种方法在挑战中获得了第5名，在数据集的封闭部分上获得了0.555的准确度@4值。
英文摘要：
This paper describes an approach to solving the next destination city recommendation problem for a travel reservation system. We propose a two stages approach: a heuristic approach for candidates selection and an attention neural network model for candidates re-ranking. Our method was inspired by listwise learning-to-rank methods and recent developments in natural language processing and the transformer architecture in particular. We used this approach to solve the Booking.com recommendations challenge Our team achieved 5th place on the challenge using this method, with 0.555 accuracy@4 value on the closed part of the dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
