On User-Generated Content (UGC) platforms, recommendation algorithms significantly impact creators' motivation to produce content as they compete for

Apple could unveil Gemini-powered Siri in Feb. TikTok's US joint venture is off to a bumpy start. UpScrolled is seeing a surge in interest. TikTok's newly formed US entity is off to a very bumpy start. As the app continues to face technical issues affecting the recommendation algorithm, view counts and other features, TikTok is also seeing a wave of frustrated users uninstalling it, according to new data. Analytics firm Sensor Tower, which tracks downloads and other app store-related metrics, told CNBC that there has been a 150 percent rise in uninstalls of the TikTok app in the United States compared with the last three months.

GPU prices could follow RAM's big rise Spotify's new playlist feature gives users more control over their recommendation algorithm Users in New Zealand will be able to write prompts for custom playlists. Spotify is attempting to give users more control over the music the streaming service recommends with a new playlist feature called Prompted Playlist. The beta feature is rolling out in New Zealand starting on December 11, and will let users write a custom prompt that Spotify can use -- alongside their listening history -- to create a playlist of new music. By tapping on Prompted Playlist, Spotify subscribers participating in the beta will be presented with a prompt field where they can type exactly what they want to hear and how they want Spotify's algorithm to respond. And while past AI features took users' individual taste into consideration, Spotify claims Prompted Playlist taps into your entire Spotify listening history, all the way back to day one.

Neural Information Processing Systems http://nips.cc/

Being often trained on implicit user feedback, many recommenders suffer from the sparsity challenge due to the lack of explicitly negative samples. The GAN-style recommenders (i.e., IRGAN) addresses the challenge by learning

This paper examines the ethical and anthropological challenges posed by AI-driven recommender systems (RSs), which increasingly shape digital environments and social interactions. By curating personalized content, RSs do not merely reflect user preferences but actively construct experiences across social media, entertainment platforms, and e-commerce. Their influence raises concerns over privacy, autonomy, and mental well-being, while existing approaches such as "algorethics" - the effort to embed ethical principles into algorithmic design - remain insufficient. RSs inherently reduce human complexity to quantifiable profiles, exploit user vulnerabilities, and prioritize engagement over well-being. The paper advances a three-dimensional framework for human-centered RSs, integrating policies and regulation, interdisciplinary research, and education. These strategies are mutually reinforcing: research provides evidence for policy, policy enables safeguards and standards, and education equips users to engage critically. By connecting ethical reflection with governance and digital literacy, the paper argues that RSs can be reoriented to enhance autonomy and dignity rather than undermine them.

Recent years have seen an explosion in the usage of social media and online social networks.

Ensuring fair outcomes for multiple stakeholders in recommender systems has been studied mostly in terms of algorithmic interventions: building new models with better fairness properties, or using reranking to improve outcomes from an existing algorithm. What has rarely been studied is structural changes in the recommendation ecosystem itself. Our work explores the fairness impact of algorithmic pluralism, the idea that the recommendation algorithm is decoupled from the platform through which users access content, enabling user choice in algorithms. Prior work using a simulation approach has shown that niche consumers and (especially) niche providers benefit from algorithmic choice. In this paper, we use simulation to explore the question of profile portability, to understand how different policies regarding the handling of user profiles interact with fairness outcomes for consumers and providers.