  Automated recommendations can nowadays be found on many e-commerce platforms,
and such recommendations can create substantial value for consumers and
providers. Often, however, not all recommendable items have the same profit
margin, and providers might thus be tempted to promote items that maximize
their profit. In the short run, consumers might accept non-optimal
recommendations, but they may lose their trust in the long run. Ultimately,
this leads to the problem of designing balanced recommendation strategies,
which consider both consumer and provider value and lead to sustained business
success. This work proposes a simulation framework based on agent-based
modeling designed to help providers explore longitudinal dynamics of different
recommendation strategies. In our model, consumer agents receive
recommendations from providers, and the perceived quality of the
recommendations influences the consumers' trust over time. We design several
recommendation strategies which either give more weight on provider profit or
on consumer utility. Our simulations show that a hybrid strategy that puts more
weight on consumer utility but without ignoring profitability considerations
leads to the highest cumulative profit in the long run. This hybrid strategy
results in a profit increase of about 20 % compared to pure consumer or profit
oriented strategies. We also find that social media can reinforce the observed
phenomena. In case when consumers heavily rely on social media, the cumulative
profit of the best strategy further increases. To ensure reproducibility and
foster future research, we publicly share our flexible simulation framework.
