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.

2002: Last.fm and Audioscrobbler Herald the Social Web

Following in Amazon's footsteps, two student projects independently use 'collaborative filtering' to bring recommendations and social networking to online music; soon they will join forces.

Paper Skygest: Personalized Academic Recommendations on Bluesky

We build, deploy, and evaluate Paper Skygest, a custom personalized social feed for scientific content posted by a user's network on Bluesky and the AT Protocol. We leverage a new capability on emerging decentralized social media platforms: the ability for anyone to build and deploy feeds for other users, to use just as they would a native platform-built feed. To our knowledge, Paper Skygest is the first and largest such continuously deployed personalized social media feed by academics, with over 50,000 weekly uses by over 1,000 daily active users, all organically acquired. First, we quantitatively and qualitatively evaluate Paper Skygest usage, showing that it has sustained usage and satisfies users; we further show adoption of Paper Skygest increases a user's interactions with posts about research, and how interaction rates change as a function of post order. Second, we share our full code and describe our system architecture, to support other academics in building and deploying such feeds sustainably. Third, we overview the potential of custom feeds such as Paper Skygest for studying algorithm designs, building for user agency, and running recommender system experiments with organic users without partnering with a centralized platform.

Bluesky "For You" feed playground

This page shows recommendations generated by the For You custom feed. The algorithm has three simple steps:

Bluesky "For You" feed playground

This page shows recommendations generated by the For You custom feed. The algorithm has three simple steps:

How Platform Recommenders Work – Center for Human-Compatible Artificial Intelligence

A recommender system (or simply ‘recommender’) is an algorithm that takes a large set of items and determines which of those to display to a user—think the Facebook News Feed, the Twitter timeline, Google News, or the YouTube homepage. Recommenders are necessary tools to help navigate the sheer volume of content produced each day, but their scale and rapid development can cause unintended consequences. Facebook’s algorithms have been blamed for radicalizing users, TikTok’s for inundating teens with eating-disorder videos, and Twitter’s for political bias. 

Understanding Social Media Recommendation Algorithms

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“Data Strikes”: Evaluating the Effectiveness of a New Form of Collective Action Against Technology Companies | The World Wide Web Conference

Collaborative recommendation is effective at representing a user's overall interests and tastes, and finding peer users that can provide good recommendations. However, it remains a challenge to make collaborative recommendation sensitive to a user's ...

Reconstructing a music recommendation model

Marco Zocca

ProbablyFrens, Contextual Feeds and Intent-Based Discovery

It’s that time of the year—ETHDenver is just around the corner! Index will be there, and if you’re attending, we’d love to connect! But that’s not all—Index has been evolving, bringing new experiences to make discovery more intuitive and personalized.Meet ProbablyFrens: The Matchmaker AgentWe’re introducing Index’s Matchmaker Agent, an autonomous connector that helps you find the friendships, collaborations, and conversations that should already exist. Whether you’re looking for thought partn...

Two tower models for retrieval of recommendations

Fourth post in this series on personalized recommendations