Two tower models for retrieval of recommendations

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.

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:

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.

substandard-recs

We'll ask permission to read your likes from the last 30 days and match you with long-form writing published on Standard.site. Your recommendations page will be public.

standard-recs

We'll ask permission to read your likes from the last 30 days and match you with long-form writing published on Standard.site. Your recommendations page will be public.

Reconstructing a music recommendation model

Marco Zocca

pub search / recommended

most-recommended posts across atproto publishing platforms

nonstandard-recs

We'll ask permission to read your likes from the last 30 days and match you with long-form writing published on Standard.site. Your recommendations page will be public.

Michael Tsai - Blog - App Store Personalized Recommendations and Keylogging

This week, Apple announced a series of discovery features that will personalize app recommendations based on users’ interests and behavior, providing a new way for developers to have their app discovered.

Deep Neural Networks for YouTube Recommendations

YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.

Introducing recommendations: simple cross-promotion for writers

A new way for writers on Substack to recommend each other and discover more great work