%0 Book Section %1 Ricci2020 %A Ricci, Francesco %B Handbook of e-Tourism %C Cham %D 2020 %E Xiang, Zheng %E Fuchs, Matthias %E Gretzel, Ulrike %E Höpken, Wolfram %I Springer International Publishing %K location-adaptive recommender tourism %P 1--18 %R 10.1007/978-3-030-05324-6_26-1 %T Recommender Systems in Tourism %U https://doi.org/10.1007/978-3-030-05324-6_26-1 %X Recommender systems (RSs) are information search and filtering tools that provide suggestions for items to be of use to a user. They are now common in many Internet applications (Google News, Amazon, TripAdvisor), helping users to make better choices while searching for news, books, or vacations. RSs exploit data mining and information retrieval techniques to predict to what extent an item fits the user needs and wants. RSs interact with the user to fine-tune these suggestions while presenting a selection of the items, among those having the largest predicted fit score. RSs have been used in tourism applications for suggesting points of interest to visit, holiday properties, and flights, or even generating complete plans for holidays, that is, bundling different types of more elementary items (e.g., accommodations and events) in one recommendation bundle. %@ 978-3-030-05324-6

@inbook{Ricci2020, abstract = {Recommender systems (RSs) are information search and filtering tools that provide suggestions for items to be of use to a user. They are now common in many Internet applications (Google News, Amazon, TripAdvisor), helping users to make better choices while searching for news, books, or vacations. RSs exploit data mining and information retrieval techniques to predict to what extent an item fits the user needs and wants. RSs interact with the user to fine-tune these suggestions while presenting a selection of the items, among those having the largest predicted fit score. RSs have been used in tourism applications for suggesting points of interest to visit, holiday properties, and flights, or even generating complete plans for holidays, that is, bundling different types of more elementary items (e.g., accommodations and events) in one recommendation bundle.}, added-at = {2020-02-19T20:44:23.000+0100}, address = {Cham}, author = {Ricci, Francesco}, biburl = {https://www.bibsonomy.org/bibtex/265c84ccf13a1b1f880b36c3a263e4c5b/brusilovsky}, booktitle = {Handbook of e-Tourism}, description = {Recommender Systems in Tourism | SpringerLink}, doi = {10.1007/978-3-030-05324-6_26-1}, editor = {Xiang, Zheng and Fuchs, Matthias and Gretzel, Ulrike and H{\"o}pken, Wolfram}, interhash = {ea7c932e33a5dacc6504fdf7431c4891}, intrahash = {65c84ccf13a1b1f880b36c3a263e4c5b}, isbn = {978-3-030-05324-6}, keywords = {location-adaptive recommender tourism}, pages = {1--18}, publisher = {Springer International Publishing}, timestamp = {2020-02-19T20:44:23.000+0100}, title = {Recommender Systems in Tourism}, url = {https://doi.org/10.1007/978-3-030-05324-6_26-1}, year = 2020 }