We approached the causal discovery task in the recommender system domain to learn a causal graph by combining observational data provided by a meta-search booking platform for online hotel search with prior knowledge made available by domain experts. The results show that it is possible to learn a causal graph coherent with previous findings in the recommender systems literature about the relations between different factors. Furthermore, we also discovered new insights that could help in the recommendation process.
Cavenaghi, E., Zanga, A., Rimoldi, A., Minasi, P., Stella, F., Zanker, M. (2023). Causal Discovery in Recommender Systems: a Case Study in Online Hotel Search. Intervento presentato a: Consequences 2023 : Causality, Counterfactuals & Sequential Decision-Making, A RecSys 2023 Workshop, Singapore, Singapore.
Causal Discovery in Recommender Systems: a Case Study in Online Hotel Search
Emanuele Cavenaghi
Primo
;Alessio ZangaSecondo
;Fabio StellaPenultimo
;
2023
Abstract
We approached the causal discovery task in the recommender system domain to learn a causal graph by combining observational data provided by a meta-search booking platform for online hotel search with prior knowledge made available by domain experts. The results show that it is possible to learn a causal graph coherent with previous findings in the recommender systems literature about the relations between different factors. Furthermore, we also discovered new insights that could help in the recommendation process.
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