Recommendation Systems - Not so discussed areas

Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time

User experience in modern content discovery applications critically depends on high-quality personalized recommendations. However, building systems that provide such recommendations presents a major challenge due to a massive pool of items, a large

Pixie: A System for Recommending 3+ Billion Items to200+ Million Users in Real-Time

User experience in modern content discovery applications critically depends on high-quality personalized recommendations. However, building systems that provide such recommendations presents a major challenge due to a …

Recommender systems and their ethical challenges

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system.

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.

metabrainz/troi-recommendation-playground

A recommendation engine playground that should hopefully make playing with music recommendations easy.

The Price of Advice: Experimental Evidence on the Effects of AI Recommenders

<p><span>The integration of large language models (LLMs) into recommender systems (RS) has given rise to a new generation of Conversational RS (CRS). This study

AI recommendation concentration by industry | Parse

Parse analyzed 631,000 AI recommendations. A category leader held 81% in some consumer markets, compared with 22% across 15 brands in B2B software.

Twitter's For You Recommendation Algorithm

Twitter has open-sourced a majority of its recommendation algorithm. It offers an exciting opportunity for researchers, industry practitioners, and RecSys enthusiasts to take a close look at how Twitter computes the recommended feed for the For You page. This article described Twitter’s end-to-end recommender system along with relevant literature and code references.

Pixie: a system for recommending 3+ billion items to 200+ million users in real-time

Pixie: a system for recommending 3+ billion items to 200+ million users in real-time Eksombatchai et al., WWW’18 (If you don’t have ACM Digital Library access, the paper can be accessed eithe…

What’s the Difference Between Search and Recommendation?

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

Collaborative filtering

Collaborative filtering (CF) is, besides content-based filtering, one of two major techniques used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.

Pixie: A System for Recommending 3+ Billion Items to 200+ Million...

User experience in modern content discovery applications critically depends on high-quality personalized recommendations. However, building systems that provide such recommendations presents a...