Practical Solutions and Value of AI-Based Recommenders
Methodologies Employed
The survey analyzes the role of recommenders in human-AI ecosystems using empirical and simulation studies. Empirical studies derive insights from real-world data, while simulation studies create synthetic data through models for controlled experimentation.
Outcomes Observed
The outcomes of AI-based recommenders are categorized into diversity, echo chambers, polarization, radicalization, inequality, and volume. These outcomes impact user behavior and societal dynamics across various online platforms.
Future Directions
The survey suggests future research in multi-disciplinary approaches, longitudinal studies, ethical considerations, and policy implications to ensure the positive development of recommender systems.
AI Solutions for Your Company
Identify Automation Opportunities
Locate key customer interaction points that can benefit from AI to streamline processes and improve customer experience.
Define KPIs
Ensure measurable impacts on business outcomes by defining key performance indicators for your AI initiatives.
Select an AI Solution
Choose AI tools that align with your specific business needs and offer customization for optimal results.
Implement Gradually
Start with a pilot AI program, gather data, and expand usage judiciously to maximize the benefits of AI for your company.
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