Temporally, users browse and interact with items in recommender systems. However, for most systems, the majority of the displayed items do not elicit any action from users. In other words, the user-system interaction process includes three aspects: browsing, action, and inaction. Prior recommender systems literature has focused more on actions than on browsing or inaction. In this work, we deployed a ield survey in a live movie recommender system to interpret what inaction means from both the user's and the system's perspective, guided by psychological theories of human decision making. We further systematically study factors to infer the reasons of user inaction and demonstrate with oline data sets that this descriptive and predictive inaction model can provide beneits for recommender systems in terms of both action prediction and recommendation timing.
|Original language||English (US)|
|Title of host publication||RecSys 2018 - 12th ACM Conference on Recommender Systems|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||9|
|State||Published - Sep 27 2018|
|Event||12th ACM Conference on Recommender Systems, RecSys 2018 - Vancouver, Canada|
Duration: Oct 2 2018 → Oct 7 2018
|Name||RecSys 2018 - 12th ACM Conference on Recommender Systems|
|Other||12th ACM Conference on Recommender Systems, RecSys 2018|
|Period||10/2/18 → 10/7/18|
Bibliographical notePublisher Copyright:
© 2018 Association for Computing Machinery.
- Decision ield theory
- Decision making
- User inaction