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., Zanker, M., Stella, F. (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
Emanuele Cavenaghi
Primo
;Alessio ZangaSecondo
;Fabio StellaUltimo
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.
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