arXiv:2412.20802v3 Announce Type: replace-cross Abstract: Recommender systems are widely used in the digital landscape to match users with content fitting their preferences. However, growing concerns about fake accounts, strategic manipulation, and other deceptive online behavior place increasing pressure on the reliability of these systems. A common statistical approach behind recommender systems is so-called matrix completion, which predicts how users would rate items they have not yet consumed based on patterns in observed ratings. Realistically applying matrix completion methods requires j
The increasing prevalence of generative AI and automated manipulation tactics necessitates more robust and reliable recommender systems to maintain platform integrity.
Reliable recommender systems are critical infrastructure for the digital economy, influencing everything from e-commerce to social media content consumption, and their integrity directly impacts trust and user experience.
This research suggests advancements in statistical methods for matrix completion can lead to more resilient recommender systems, thereby improving their ability to withstand deceptive online behaviors.
- · E-commerce platforms
- · Social media companies
- · AI/ML researchers
- · Consumers
- · Malicious actors
- · Fake account networks
Improved recommender system reliability leads to better user experience and reduced impact from misinformation or fraudulent activities.
Higher trust in digital platforms could increase user engagement and economic activity facilitated by these systems.
Enhanced defense against manipulation could shift the tactics of bad actors, potentially accelerating an arms race in digital security and deception.
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Read at arXiv cs.LG