A Link Prediction Framework for Hotel Recommendations


Sevim Y., ORMAN G. K., Kılıçlıoğlu O. M.

2022 World Congress on Engineering, WCE 2022, London, İngiltere, 6 - 08 Temmuz 2022, cilt.2244, ss.64-69 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 2244
  • Basıldığı Şehir: London
  • Basıldığı Ülke: İngiltere
  • Sayfa Sayıları: ss.64-69
  • Anahtar Kelimeler: Link Prediction, Node Similarity, Precision, Smart Tourism
  • Galatasaray Üniversitesi Adresli: Evet

Özet

© 2022 Newswood Limited. All rights reserved.Successfully predicting rarely occurring events in large systems can be extremely valuable in various scenarios, such as fraud detection, quality control, sales prediction, etc. In tourism, predicting connections between hotels via their similarity scores can form the basis of a hotel recommendation engine. In this work, we propose a link prediction framework for such an application. This framework first extracts a hotel-to-hotel network from hotel-customer raw data sets. Then, it applies various link prediction approaches. Besides employing well-known node similarity metrics such as Adamic Adar, Jaccard Coefficient, and Preferential Attachment, we also contribute to developing their weighted versions. These six metrics are executed in the basic supervised task of link prediction. The results are evaluated by precision and AUC. In our experiments, we used two novel data sets from the tourism sector: SeturTech and Otelpuan. The results demonstrate that the proposed weighted Adamic Adar returns the most accurate link predictions.