Index

OpenSkill: Multiplayer Rating System. No Friction. In the multifaceted world of online gaming, an accurate multiplayer rating system plays a crucial role. A multiplayer rating system measures and c...

Avoid Blunders Designing Reputation Systems

You don’t SPEND reputation because ratings are orthogonal to exchange.

Community newsletters rating | Notion

Here is where you can read some great stuff about community 🤩

Rating all the things | Tynan Is Typing | Tynan is afk til 10/19 ⁂ | Offprint

All existing proposals are wrong because they are all right

A/B/U Review System - ORI Onboarding

This is the rating system we use at ORI. Given a piece of information, rate it according to the following rubric:

Rainbow Slug

56K votes, 468 comments. 20M subscribers in the Damnthatsinteresting community. For the most interesting things on the internet

A turtle giving itself a scrub

202K votes, 1.8K comments. 13M subscribers in the oddlysatisfying community. For those little things that are inexplicably satisfying.

Ranking Strategies on Farcaster | OpenRank

Profile reputation scoring to address trustworthiness in social networks

Your Book Review: We Present Television

Finalist #11 in the Book Review Contest

Going viral sucks (even more) now

We’ve out-algorithmed ourselves.

Feedbackmaxxing

You know the TV gameshow Play Your Cards Right? Contestants are shown a sequence – in two rows – of giant playing cards presented face-down. The host turns over the first card. The cont…

[deleted by user]

85 votes, 61 comments. 140K subscribers in the browsers community. Topics on web browsers.

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.