The LikeMinds Feed is a dynamic content delivery system designed to ensure that users receive the most relevant and engaging posts. It operates through four different types of feeds, each catering to specific user needs and content delivery strategies. These are:

1. Universal Feed – A simple, chronologically sorted feed.
2. Ranked Feed – A feed ranked based on user engagement and recency.
3. Personalized Feed – A highly customized feed based on user preferences and behaviors.
4. Topic Feed – A feed aggregated based on the topic selected/followed by the user.

Let's understand each feed in more detail – their sorting mechanisms and how they contribute to the overall LikeMinds experience.

1. Universal Feed

Overview

The Universal Feed is the simplest feed type, where posts are displayed in the order they were created. This feed ensures that users see the latest content first without any ranking modifications.

Sorting Algorithm

• Posts are arranged in reverse chronological order (newest to oldest).
• No influence of engagement metrics like likes or comments.

Use Cases

• Feed applications that prefer a raw and unfiltered timeline.
• Users who want to see the latest content without algorithmic ranking.
• Ideal for real-time updates where recency is the key factor.

2. Ranked Feed

Overview

The Ranked Feed enhances user experience by prioritizing posts based on engagement and freshness. This approach ensures that the most interacted-with content gains higher visibility.

Sorting Algorithm

The feed ranks posts using a scoring system based on three key metrics:

1. Likes – Higher liked posts receive a better ranking.
2. Comments – Posts with more comments are considered more engaging.
3. Recency – Newer posts are given a ranking boost to maintain freshness.

The ranking formula can be represented as:‍

Score = (Likes/MaxLikes)* LikesWeight+(Comments/MaxComments)* CommentsWeight+(Recency/MaxRecency)*
RecencyWeight

Where:

Likes

• Likes → Number of likes on the post
• MaxLikes → The highest possible number of likes considered for ranking
  • Beyond this limit, the Likes count contributes the highest possible score to the ranking score.
  • This can be customised based on the customer’s requirement.
• LikesWeight → The weight assigned to likes
  • This can be customised based on the customer’s requirement.

Comments

• Comments → Number of comments on the post
• MaxComments → The highest possible number of comments considered for ranking
  • Beyond this limit, the Comments count contributes the highest possible score to the ranking score.
  • This can be customised based on the customer’s requirement.
• CommentsWeight → The weight assigned to comments
  • This can be customised based on the customer’s requirement.

Recency

• Recency → How recently the post was created (in minutes)
• MaxRecency → The highest possible recency value considered for ranking
  • Beyond this limit, the recency metric will contribute a nil score to the ranking score.
  • The unit of MaxRecency is in minutes.
  • This can be customised based on the customer’s requirement.
• RecencyWeight → The weight assigned to recency
  • The recency factor follows a time decay function, ensuring that older posts gradually lose rank over time.
  • This can be customised based on the customer’s requirement.

Use Cases

• Feed Applications that want to highlight trending discussions.
• Users who prefer seeing popular posts rather than purely the latest ones.

3. Personalized Feed

Overview

The Personalized Feed takes customization to the next level by tailoring content based on user preferences and interactions. It builds upon the Ranked Feed by incorporating user-specific behaviors.

Sorting Algorithm

The personalization algorithm enhances ranking by adding three more dimensions to the standard ranking feed:

1. Topics Followed – Posts from topics a user follows receive a ranking boost.
2. Users Followed – Posts from users a person follows are prioritized.
3. Dampening – Posts seen by a person are deprioritized in their feed.

The ranking formula expands to:

Score = Ranking feed Score + (Topics Boost * Topics Weight)+(Users Boost * Users Weight) + Post Seen

Where:

Ranking Feed Score

• The score is calculated based on the Ranked Feed Sorting Algorithm.

Topics Boost

• Topics Followed → The topics a person follows
• TopicsWeight → The weight assigned to followed topics in ranking
  • This can be customised based on the customer’s requirement. ‍

Users Boost

• Users Followed → The users a person follows
• UsersWeight → The weight assigned to followed users in ranking
  • This can be customised based on the customer’s requirement.

Post Seen

• Post Seen → Posts seen by a user are dampened for the user’s feed
• DampeningDuration → Dampened posts can reappear once the dampening duration expires

Use Cases

• Feed Application where users have diverse interests.
• Personalized content delivery for higher engagement.
• Users who want a balance between trending and relevant content.

4. Topic Feed

Overview

The Topic Feed is an aggregation of posts linked with topics that are followed by users.

Use Cases

• Feed Application where the customer wants their user to see specific posts as per the followed topics.
• Content library where there are a lot of resources and require tagging as per the given topics.

There will be more to follow on this in our upcoming blogs.

Conclusion

The LikeMinds Feed system provides flexibility in content discovery through its three-tiered approach. While the Universal Feed ensures real-time updates, the Ranked Feed enhances the visibility of popular content, and the Personalized Feed ensures that highly relevant posts reach the user. The combination of these approaches allows LikeMinds to cater to a wide range of community needs, improving engagement and user satisfaction.