paper: https://joisino.net/papers/catalyst/ Introduction The web is home to many large platforms, including video-sharing and social networking services. These platforms keep users firmly locked in through factors such as the data users have accumulated on them and the network effects arising from the presence of large numbers of other users. This makes it difficult for better […]
iAgent [Xu et al., ACL 2025] proposes placing an agent between a provider-side recommender system and the user. The agent searches for relevant knowledge and reranks the recommendations produced by the platform, with the goal of obtaining rankings that better reflect the user’s preferences. The paper motivates this approach as follows: Traditional recommender systems usually
When you start using a new video or music service, the first recommendations often miss the mark, and it can be surprisingly hard to settle in. This is the cold start problem. Traditionally, we treat it as something the provider should solve, while the user can only “keep using it and wait until the system
Break the Lock-In: Carry Your Preferences Anywhere Read Post »
“Recommended for You” YouTube, Amazon, Netflix, X, news sites… We are surrounded by services displaying recommendations. While convenient, have you ever felt, “I keep getting recommended the same kinds of things,” or “I wish I could get recommendations from a different perspective”? Or perhaps wondered, “Are these recommendations biased?” The truth is, these “recommendations” (recommender
Hack Your Feed: Take Control of Your Recommendations Read Post »
