Reddit 的个性化模型：深入探讨

在上一篇文章中，我们讨论了 X 或 Twitter 的个性化模型的工作原理。这篇文章将尝试将 Reddit 的个性化模型与 X 的个性化模型并列，以便我们对个性化模型有一个连贯的理解。

In the previous post, we discussed how X or Twitter’s Personalization Model works. This post will attempt to juxtapose Reddit’s personalization model against X’s so that we develop a coherent understanding of personalization models.

在上一篇文章中，我们讨论了 X 或 Twitter 的个性化模型的工作原理。这篇文章将尝试将 Reddit 的个性化模型与 X 的个性化模型并列，以便我们对个性化模型有一个连贯的理解。

How X & Reddit are similar: X and Reddit both thrive on user-generated, community-driven content and engagement algorithms, enabling real-time discussions and content virality. In fact, Reddit is the only other social network that Apple’s App Store also classifies as a news product.

X 和 Reddit 的相似之处：X 和 Reddit 都依靠用户生成、社区驱动的内容和参与算法蓬勃发展，从而实现实时讨论和内容病毒式传播。事实上，Reddit 是苹果应用商店中唯一一个也被归类为新闻产品的社交网络。

How X & Reddit are different: Reddit is a network of 100k subreddits centered on shared interests, while X emphasizes individual user accounts providing brief, real-time updates.

X 和 Reddit 有何不同：Reddit 是一个由 10 万个 Reddit 子版块组成的网络，以共同兴趣为中心，而 X 则强调提供简短实时更新的个人用户帐户。

Historically, personalization on Reddit:

从历史上看，Reddit 上的个性化：

In July 2021, Reddit introduced a personalized feed: Instead of recommending subreddits, they started recommending posts directly in the user’s feed.

2021 年 7 月，Reddit 推出了个性化 Feed：他们开始直接在用户的 Feed 中推荐帖子，而不是推荐 Reddit 子版块。

With this context out of the way, let’s get into how they’ve built it by placing Reddit’s model in the six stages that we introduced in the previous blog — Twitter’s Personalization Model:

抛开这个背景，让我们通过将 Reddit 的模型置于我们在上一篇博客中介绍的六个阶段（Twitter 的个性化模型）来了解他们是如何构建它的：

1. Selection from the Corpus

1. 从语料库中选择

The system starts with all Reddit submissions from the past 24 hours.

该系统从过去 24 小时内所有 Reddit 提交内容开始。

2. Candidate Generation

2. 候选人生成

It then uses machine learning to identify posts from subreddits you’ve joined, subreddits similar to those you’ve joined, or subreddits you’ve visited recently. For diversity, it also recommends posts from subreddits that are popular or geographically popular.

然后，它使用机器学习来识别您已加入的 Reddit 子版块、与您已加入的 Reddit 子版块类似的 Reddit 子版块或您最近访问过的 Reddit 子版块中的帖子。为了多样性，它还推荐来自流行或地理上流行的 Reddit 子版块的帖子。

3. Filtering

3. 过滤

They remove posts that are:

他们删除的帖子是：

4. Scoring

4. 评分

A ML model assigns a weighted-score to each of the remaining posts by probability of click (CTR), propensity of joining (or leaving) the subreddit, propensity of commenting or upvoting/downvoting the post and watch probability if the post has a video.

机器学习模型根据点击概率 (CTR)、加入（或离开）reddit 子版块的倾向、评论或对帖子投赞成票/反对票的倾向以及观看帖子（如果帖子有视频）的概率，为每个剩余帖子分配加权分数。

Below are some interesting quotes from Reddit blogs:

以下是来自 Reddit 博客的一些有趣的引述：

Multi-task models have become particularly important at Reddit. Users engage with content in many ways, with many content types, and their engagement tells us what content and communities they value.

多任务模型在 Reddit 上变得尤为重要。用户以多种方式、多种内容类型参与内容，他们的参与告诉我们他们看重哪些内容和社区。

This type of training also implicitly captures negative feedback – content the user chose not to engage with, downvotes, or communities they unsubscribe from.

这种类型的培训还隐式地捕捉负面反馈——用户选择不参与的内容、否决票或他们取消订阅的社区。

These probabilities can be used to estimate long term measures such as retention.

这些概率可用于估计长期指标，例如保留率。

5. Re-ranking

5. 重新排名

At this point, Reddit doesn’t blindly always put the posts with the highest score at the top. Instead, they use sampling to inject:

此时，Reddit 并不总是盲目地将得分最高的帖子放在顶部。相反，他们使用采样来注入：

The feed is curated to avoid showing too many similar posts in a row. Even if several posts have high scores, they might be spaced apart to enhance variety. Posts from different subreddits, topics, and formats (e.g., text, video, link) are interspersed to keep the feed engaging.

该提要经过精心设计，以避免连续显示太多类似的帖子。即使几个帖子得分很高，它们也可能会分开以增强多样性。来自不同子版块、主题和格式（例如文本、视频、链接）的帖子散布在一起，以保持提要的吸引力。

Conclusion

结论

I will continue reviewing additional product literature on personalization models employed across various media products, but it is likely that the six stages mentioned above will remain applicable.

我将继续回顾有关各种媒体产品中采用的个性化模型的其他产品文献，但上述六个阶段很可能仍然适用。

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Curious how I’m managing to write? I created a CustomGPT for myself, which serves as my go-to editor and audits my first draft. Here’s the link—give it a spin! It’s free to use.

好奇我是如何写作的吗？我为自己创建了一个 CustomGPT，它作为我的首选编辑器并审核我的初稿。这是链接——试一试！它可以免费使用。

https://chatgpt.com/g/g-hgI62sWPm-mediaflywheels-review-opinion-pieces

Want to republish it? This post was released under CC BY-ND — you can republish it as is with the following credit and backlinks: ‘Originally published by Ritvvij Parrikh on The Times of India. The author retains the copyright and any other ancillary rights to the post.

想要重新发布吗？这篇文章是在 CC BY-ND 下发布的——您可以按原样重新发布，并带有以下来源和反向链接：“最初由 Ritvvij Parrikh 在《印度时报》上发布。”作者保留该帖子的版权和任何其他附属权利。