Recommender systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by many factors when making decisions, and the recommender must account for these to be effective. In this work, we use a causal graph to investigate the influence of different factors on the user's decision to click or not on the recommended accommodations. To learn the causal graph, we combine data provided by a meta-search booking platform for online hotel searches with prior knowledge made available by domain experts. The analysis confirms that the learnt causal model correctly models the well-known effect of the ranking position and price on user decision-making. Furthermore, we discover some interactions between the considered factors. For example, the country of the user market influences the user's decisions for different values of the price.

Cavenaghi, E., Zanga, A., Rimoldi, A., Minasi, P., Stella, F., Zanker, M. (2023). Analysis of Relevant Factors in Online Hotel Recommendation Through Causal Models. In Proceedings of the Workshop on Recommenders in Tourism co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023) (pp.1-9). CEUR-WS.

Analysis of Relevant Factors in Online Hotel Recommendation Through Causal Models

Zanga A.
Secondo
;
Stella F.;
2023

Abstract

Recommender systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by many factors when making decisions, and the recommender must account for these to be effective. In this work, we use a causal graph to investigate the influence of different factors on the user's decision to click or not on the recommended accommodations. To learn the causal graph, we combine data provided by a meta-search booking platform for online hotel searches with prior knowledge made available by domain experts. The analysis confirms that the learnt causal model correctly models the well-known effect of the ranking position and price on user decision-making. Furthermore, we discover some interactions between the considered factors. For example, the country of the user market influences the user's decisions for different values of the price.
paper
Causal Networks; Meta-search Booking Platform; Online Hotel Search; Recommender Systems; Tourism;
English
RecTour 2023 Workshop on Recommenders in Tourism 2023 - September 19, 2023
2023
Neidhardt, J; Wörndl, W; Kuflik, T; Goldenberg, D; Zanker, M
Proceedings of the Workshop on Recommenders in Tourism co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023)
2023
3568
1
9
https://ceur-ws.org/Vol-3568/
open
Cavenaghi, E., Zanga, A., Rimoldi, A., Minasi, P., Stella, F., Zanker, M. (2023). Analysis of Relevant Factors in Online Hotel Recommendation Through Causal Models. In Proceedings of the Workshop on Recommenders in Tourism co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023) (pp.1-9). CEUR-WS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/444760
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