%0 Conference Paper %1 Hazrati_2022 %A Hazrati, Naieme %A Ricci, Francesco %B Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization %D 2022 %I ACM %K evaluation recommender simulation umap2022 %P 95-98 %R 10.1145/3511047.3536402 %T Simulating Users' Interactions with Recommender Systems %U https://doi.org/10.1145%2F3511047.3536402 %X Web platforms, such as a video-on-demand services or eCommerce sites, are routinely using Recommender System (RS) to help their users in choosing which item to consume or buy. It is therefore important to understand how the exposure to recommendations can influence the users’ choices and, consequently, the RS’s performance. Important metrics to consider are related to the quality and distribution of the chosen items. This important research focus calls for novel evaluation approaches. A relevant and emerging line of research is based on the simulation of users’ choice behaviour when exposed to recommendations. Simulation-based studies have shown to be useful tools for understanding how an RS performs and its users behave, now and in the future, under various conditions. This paper offers a broad perspective on the field and discusses the potential of simulations in unlocking certain types of analysis that are infeasible by other means. We also discuss the limitations of the current simulation studies.

@inproceedings{Hazrati_2022, abstract = {Web platforms, such as a video-on-demand services or eCommerce sites, are routinely using Recommender System (RS) to help their users in choosing which item to consume or buy. It is therefore important to understand how the exposure to recommendations can influence the users’ choices and, consequently, the RS’s performance. Important metrics to consider are related to the quality and distribution of the chosen items. This important research focus calls for novel evaluation approaches. A relevant and emerging line of research is based on the simulation of users’ choice behaviour when exposed to recommendations. Simulation-based studies have shown to be useful tools for understanding how an RS performs and its users behave, now and in the future, under various conditions. This paper offers a broad perspective on the field and discusses the potential of simulations in unlocking certain types of analysis that are infeasible by other means. We also discuss the limitations of the current simulation studies.}, added-at = {2022-07-25T04:39:05.000+0200}, author = {Hazrati, Naieme and Ricci, Francesco}, biburl = {https://www.bibsonomy.org/bibtex/21e2a6639e3e511d13614e216c5f9b83d/brusilovsky}, booktitle = {Adjunct Proceedings of the 30th {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {Simulating Users’ Interactions with Recommender Systems | Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3511047.3536402}, interhash = {275bcbe625b530da9aebedd189699984}, intrahash = {1e2a6639e3e511d13614e216c5f9b83d}, keywords = {evaluation recommender simulation umap2022}, month = jul, pages = {95-98}, publisher = {{ACM}}, timestamp = {2022-08-01T16:54:22.000+0200}, title = {Simulating Users' Interactions with Recommender Systems}, url = {https://doi.org/10.1145%2F3511047.3536402}, year = 2022 }