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Tailoring recommendation algorithms to ideal preferences makes users better off.

Poruz Khambatta1, Shwetha Mariadassou2, Joshua Morris2

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Recommendation algorithms can help users achieve ideal preferences, not just actual ones. Tailoring recommendations to personal ideals benefits both users and companies, improving satisfaction and engagement.

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Area of Science:

  • Behavioral economics
  • Human-computer interaction
  • Computational social science

Background:

  • Individuals often experience a conflict between actual and ideal preferences, hindering goal achievement.
  • Current recommendation algorithms, by prioritizing engagement, may worsen this conflict.
  • Personalized recommendations can be optimized to align with users' aspirational goals.

Purpose of the Study:

  • To investigate whether tailoring recommendation algorithms to ideal preferences, rather than actual preferences, benefits users and companies.
  • To evaluate the impact of preference-aligned recommendation systems on user satisfaction and business metrics.

Main Methods:

  • Developed personalized recommendation systems targeting either actual or ideal preferences.
  • Conducted a large-scale, pre-registered experiment with 6,488 participants.
  • Measured user outcomes including satisfaction, perceived value, and willingness to pay.

Main Results:

  • Recommending based on ideal preferences led to fewer clicks but increased user-reported well-being and time satisfaction.
  • Targeting ideal preferences enhanced users' willingness to pay and trust in the company.
  • Users were more likely to continue using services that catered to their ideal preferences.

Conclusions:

  • Shifting recommendation algorithms from maximizing engagement to fostering ideal preferences offers significant advantages.
  • Aligning digital nudges with users' aspirations can create a more beneficial experience for both consumers and providers.
  • Future recommendation systems should aim to understand and support users' long-term goals and ideals.