Feedback Clustering for Online Travel Agencies Searches: a Case Study

TL;DR

This paper introduces a novel feedback clustering algorithm called Split-or-Merge (S/M) for segmenting online travel agency flight searches, demonstrating its effectiveness on real-world data and highlighting its potential in personalized travel recommendations.

Contribution

The paper presents the first application of the Split-Merge-Evolve clustering method to OTA flight search data, proposing a new S/M algorithm tailored for feedback-driven segmentation.

Findings

S/M algorithm outperforms traditional clustering in domain-specific metrics.

Feedback clustering enhances segmentation quality in OTA flight searches.

Experimental results confirm the effectiveness of the proposed method on real data.

Abstract

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.