%0 Conference Paper %1 Najafian_2020 %A Najafian, Shabnam %A Herzog, Daniel %A Qiu, Sihang %A Inel, Oana %A Tintarev, Nava %B Proceedings of the 31st ACM Conference on Hypertext and Social Media %D 2020 %I ACM %K explanation group-recommendation ht2020 jbpaws recommender tourism %R 10.1145/3372923.3404800 %T You Do Not Decide for Me! Evaluating Explainable Group Aggregation Strategies for Tourism %U https://doi.org/10.1145%2F3372923.3404800 %X Most recommender systems propose items to individual users. However, in domains such as tourism, people often consume items in groups rather than individually. Different individual preferences in such a group can be difficult to resolve, and often compromises need to be made. Social choice strategies can be used to aggregate the preferences of individuals. We evaluated two explainable modified preference aggregation strategies in a between-subject study (n=200), and compared them with two baseline strategies for groups that are also explainable, in two scenarios: high divergence (group members with different travel preferences) and low divergence (group members with similar travel preferences). Generally, all investigated aggregation strategies performed well in terms of perceived individual and group satisfaction and perceived fairness. The results also indicate that participants were sensitive to a dictator-based strategy, which affected both their individual and group satisfaction negatively (compared to the other strategies).

@inproceedings{Najafian_2020, abstract = {Most recommender systems propose items to individual users. However, in domains such as tourism, people often consume items in groups rather than individually. Different individual preferences in such a group can be difficult to resolve, and often compromises need to be made. Social choice strategies can be used to aggregate the preferences of individuals. We evaluated two explainable modified preference aggregation strategies in a between-subject study (n=200), and compared them with two baseline strategies for groups that are also explainable, in two scenarios: high divergence (group members with different travel preferences) and low divergence (group members with similar travel preferences). Generally, all investigated aggregation strategies performed well in terms of perceived individual and group satisfaction and perceived fairness. The results also indicate that participants were sensitive to a dictator-based strategy, which affected both their individual and group satisfaction negatively (compared to the other strategies).}, added-at = {2020-07-14T20:04:10.000+0200}, author = {Najafian, Shabnam and Herzog, Daniel and Qiu, Sihang and Inel, Oana and Tintarev, Nava}, biburl = {https://www.bibsonomy.org/bibtex/2329a4c74c44a076079b349a200714d6a/brusilovsky}, booktitle = {Proceedings of the 31st {ACM} Conference on Hypertext and Social Media}, description = {You Do Not Decide for Me! Evaluating Explainable Group Aggregation Strategies for Tourism | Proceedings of the 31st ACM Conference on Hypertext and Social Media}, doi = {10.1145/3372923.3404800}, interhash = {6c44c4aa152d2ae2c206926a99855005}, intrahash = {329a4c74c44a076079b349a200714d6a}, keywords = {explanation group-recommendation ht2020 jbpaws recommender tourism}, month = jul, publisher = {{ACM}}, timestamp = {2020-11-22T23:56:48.000+0100}, title = {You Do Not Decide for Me! Evaluating Explainable Group Aggregation Strategies for Tourism}, url = {https://doi.org/10.1145%2F3372923.3404800}, year = 2020 }