Letting Users Choose Recommender Algorithms: An Experimental Study

Recommender systems are not one-size-fits-all; different algorithms and data sources have different strengths, making them a better or worse fit for different users and use cases. As one way of taking advantage of the relative merits of different algorithms, we gave users the ability to change the algorithm providing their movie recommendations and studied how they make use of this power. We conducted our study with the launch of a new version of the MovieLens movie recommender that supports multiple recommender algorithms and allows users to choose the algorithm they want to provide their recommendations. We examine log data from user interactions with this new feature to understand whether and how users switch among recommender algorithms, and select a final algorithm to use. We also look at the properties of the algorithms as they were experienced by users and examine their relationships to user behavior.

Agentic Taste Modeling | lab notes #8

We built a forecasting benchmark to test how well agents predict what you like.

A Bluesky feed for one · Adam Wiggins

I built a custom feed for Bluesky that trains a model on my past interactions. Here's what I learned about RecSys and social media feeds generally.

How the Substack feed is learning to understand your reading journey

Modeling sequences of user behavior makes discovery feel alive