Choosing the Right Weights: Balancing Value, Strategy, and Noise in
  Recommender Systems
Smitha Milli Emma Pierson Nikhil Garg
Many recommender systems are based on optimizing a linear weighting of
different user behaviors, such as clicks, likes, shares, etc. Though the choice
of weights can have a significant impact, there is little formal study or
guidance on how to choose them. We analyze the optimal choice of weights from
the perspectives of both users and content producers who strategically respond
to the weights. We consider three aspects of user behavior: value-faithfulness
(how well a behavior indicates whether the user values the content),
strategy-robustness (how hard it is for producers to manipulate the behavior),
and noisiness (how much estimation error there is in predicting the behavior).
Our theoretical results show that for users, upweighting more value-faithful
and less noisy behaviors leads to higher utility, while for producers,
upweighting more value-faithful and strategy-robust behaviors leads to higher
welfare (and the impact of noise is non-monotonic). Finally, we discuss how our
results can help system designers select weights in practice.