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.
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Index Terms
- Simulating the Impact of Recommender Systems on the Evolution of Collective Users' Choices
Recommendations
Simulating Users’ Interactions with Recommender Systems
UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and PersonalizationWeb 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 ...
Recommender systems and their impact on sales diversity
EC '07: Proceedings of the 8th ACM conference on Electronic commerceThis paper examines the effect of recommender systems on the diversity of sales. Two anecdotal views exist about such effects. Some believe recommenders help consumers discover new products and thus increase sales diversity. Others believe recommenders ...
Choice models and recommender systems effects on users’ choices
AbstractNowadays, the users of a web platform, such as a video-on-demand service or an eCommerce site, are routinely using the platform’s recommender system (RS) when choosing which item to consume or buy (e.g. movies or books). It is therefore important ...
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