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Many recommender systems are based on optimizing a linear weighting ofdifferent user behaviors, such as clicks, likes, shares, etc. Though the choiceof weights can have a significant impact, there is little formal study orguidance on how to choose them. We analyze the optimal choice of weights fromthe perspectives of both users and content producers who strategically respondto 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-faithfuland less noisy behaviors leads to higher utility, while for producers,upweighting more value-faithful and strategy-robust behaviors leads to higherwelfare (and the impact of noise is non-monotonic). Finally, we discuss how ourresults can help system designers select weights in practice.

Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems