recommender systems

Aggregation Theory

About | Articles | Updates | Speaking | Consulting What is Aggregation Theory? Aggregation Theory is a completely new way to understand business in the Internet age. Business schools suggest that w…

What’s the Difference Between Search and Recommendation? | TechPolicy.Press

It’s hard to draw the sort of clean line you’d want when designing regulation, say Luke Thorburn, Jonathan Stray, and Priyanjana Bengani.

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...

How Platform Recommenders Work

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. 

Safety and Privacy center

At Spotify, we aim to create great and unique experiences for each user. Our goal is to connect everyone with what they love and help them discover something new. No two listeners are the same, so everyone's Spotify experience, and many of our recommendations, are personalized. When asked what they like about Spotify, most listeners cite our personalization as their top feature. You might wonder how we generate these recommendations across the Home feed, playlists, search results or other parts of the service, and we want to help demystify how they work.