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Quality News – Towards a fairer ranking algorithm for Hacker News

Hacker News

Quality News – Towards a fairer ranking algorithm for Hacker News

Hello HN! TLDR; - Quality News is a Hacker News client that provides additional data and insights on submissions, notably, the upvoteRate metric. - We propose that this metric could be used to improve the Hacker News ranking score. - In-depth explanation: https://github.com/social-protocols/news#readme The Hacker News ranking score is directly proportional to upvotes, which is a problem because it creates a feedback loop: higher rank leads to more upvotes leads to higher rank, and so on... → ↗ ↘ Higher Rank More Upvotes ↖ ↙ ← As a consequence, success on HN depends almost entirely on getting enough upvotes in the first hour or so to make the front page and get caught in this feedback loop. And getting these early upvotes is largely a matter of timing, luck, and moderator decisions. And so the best stories don't always make the front page, and the stories on the front page are not always the best. Our proposed solution is to use upvoteRate instead of upvotes in the ranking formula. upvoteRate is an estimate of how much more or less likely users are to upvote a story compared to the average story, taking account how much attention the story as received, based on a history of the ranks and times at which it has been shown. You can read about how we calculate this metric in more detail here: https://github.com/social-protocols/news#readme About 1.5 years ago, we published an article with this basic idea of counteracting the rank-upvotes feedback loop by using attention as negative feedback. We received very valuable input from the HN community ( https://news.ycombinator.com/item?id=28391659 ). Quality News has been created based largely on this feedback. Currently, Quality News shows the upvoteRate metric for live Hacker News data, as well as charts of the rank and upvote history of each story. We have not yet implemented an alternative ranking algorithm, because we don't have access to data on flags and moderator actions, which are a major component of the HN ranking score. We'd love to see the Hacker News team experiment with the new formula, perhaps on an alternative front page. This will allow the community to evaluate whether the new ranking formula is an improvement over the current one. We look forward discussing our approach with you! Links: Site: https://news.social-protocols.org/ Readme: https://github.com/social-protocols/news#readme Previous Blog Post: https://felx.me/2021/08/29/improving-the-hacker-news-ranking... Previous Discussion: https://news.ycombinator.com/item?id=28391659

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: lua, hacker news, ide · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos
46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
24%24% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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