%0 Conference Paper %1 Hazrati_2020 %A Hazrati, Naieme %A Elahi, Mehdi %A Ricci, Francesco %B Proceedings of the 31st ACM Conference on Hypertext and Social Media %D 2020 %I ACM %K evaluation ht2020 recommender simulation user-choice user-study %P 207-212 %R 10.1145/3372923.3404812 %T Simulating the Impact of Recommender Systems on the Evolution of Collective Users\textquotesingle Choices %U https://doi.org/10.1145%2F3372923.3404812 %X The major focus of recommender systems (RSs) research is on improving the goodness of the generated recommendations. Less attention has been dedicated to understand the effect of an RS on the actual users' choices. Hence, in this paper, we propose a novel simulation model of users' choices under the influence of an RS. The model leverages real rating/choice data observed up to a point in time in order to simulate next, month-by-month, choices of the users. We have analysed choice diversity, popularity and utility and found that: RSs have different effects on the users' choices; the behaviour of new users is particularly important to understand collective choices; and the users' previous knowledge, i.e., their äwareness" of the item catalogue greatly affects choice diversity.

@inproceedings{Hazrati_2020, abstract = {The major focus of recommender systems (RSs) research is on improving the goodness of the generated recommendations. Less attention has been dedicated to understand the effect of an RS on the actual users' choices. Hence, in this paper, we propose a novel simulation model of users' choices under the influence of an RS. The model leverages real rating/choice data observed up to a point in time in order to simulate next, month-by-month, choices of the users. We have analysed choice diversity, popularity and utility and found that: RSs have different effects on the users' choices; the behaviour of new users is particularly important to understand collective choices; and the users' previous knowledge, i.e., their "awareness" of the item catalogue greatly affects choice diversity. }, added-at = {2020-07-14T20:54:03.000+0200}, author = {Hazrati, Naieme and Elahi, Mehdi and Ricci, Francesco}, biburl = {https://www.bibsonomy.org/bibtex/24df21405bb908275f7f24001af2523eb/brusilovsky}, booktitle = {Proceedings of the 31st {ACM} Conference on Hypertext and Social Media}, description = {Simulating the Impact of Recommender Systems on the Evolution of Collective Users' Choices | Proceedings of the 31st ACM Conference on Hypertext and Social Media}, doi = {10.1145/3372923.3404812}, interhash = {88360e4fd2dd48b01d7f8f26b2d235fb}, intrahash = {4df21405bb908275f7f24001af2523eb}, keywords = {evaluation ht2020 recommender simulation user-choice user-study}, month = jul, pages = {207-212}, publisher = {{ACM}}, timestamp = {2022-07-25T04:15:36.000+0200}, title = {Simulating the Impact of Recommender Systems on the Evolution of Collective Users{\textquotesingle} Choices}, url = {https://doi.org/10.1145%2F3372923.3404812}, year = 2020 }