Understanding choices performed by online customers is a growing need in the travel industry. In many practical situations, the only available information is the flight search query performed by the customer with no additional profile knowledge. In general, customer flight bookings are driven by prices, duration, number of connections, and so on. However, not all customers might assign the same importance to each of those criteria. Here comes the need of grouping together all flight searches performed by the same kind of customer, that is having the same booking criteria. The effectiveness of some set of recommendations, for a single cluster, can be measured in terms of the number of bookings historically performed. This effectiveness measure plays the role of a feedback, that is an external knowledge which can be recombined to iteratively obtain a final segmentation. In this paper, we describe our Online Travel Agencies (OTA) flight search use case and highlight its specific features. We address the flight search segmentation problem motivated above by proposing a novel algorithm called Split-or-Merge (S/M). This algorithm is a variation of the Split-Merge-Evolve (SME) method. The SME method has already been introduced in the community as an iterative process updating a clustering given by the K-means algorithm by splitting and merging clusters subject to feedback independent evaluations. No previous application of the SME method to the real-word data is reported in literature to the best of our knowledge. Here, we provide experimental evaluations over real-world data to the SME and the S/M methods. The impact on our domain-specific metrics obtained under the SME and the S/M methods suggests that feedback clustering techniques can be very promising in the handling of the domain of OTA flight searches.

中文翻译：

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在线旅行社搜索的反馈聚类：案例研究

了解在线客户的选择是旅游业日益增长的需求。在许多实际情况下，唯一可用的信息是客户在没有额外资料知识的情况下执行的航班搜索查询。一般而言，客户航班预订受价格、持续时间、转机次数等因素影响。但是，并非所有客户都可能为这些标准中的每一个分配相同的重要性。这就需要将同一类客户执行的所有航班搜索组合在一起，即具有相同的预订标准。对于单个集群，某些推荐集的有效性可以根据历史上执行的预订数量来衡量。这种有效性测量起到了反馈的作用，这是一个外部知识，可以重新组合以迭代地获得最终分割。在本文中，我们描述了我们的在线旅行社 (OTA) 航班搜索用例并重点介绍了其特定功能。我们通过提出一种称为 Split-or-Merge (S/M) 的新算法来解决上述的航班搜索分割问题。该算法是 Split-Merge-Evolve (SME) 方法的一种变体。SME 方法已经被引入社区，作为一个迭代过程，通过分裂和合并受反馈独立评估的集群来更新 K-means 算法给出的集群。据我们所知，文献中没有报道过以前将 SME 方法应用于实词数据。在这里，我们为 SME 和 S/M 方法提供对真实世界数据的实验评估。