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Content Rating Pattern

How to create a Content rating algorithm

How content rating algorithm works - How to code likes, votes, hot topics and ratings

Wherever there is content of some sort in text, sound, voice, thoughts, images, perceptions there will also be opinions about the content. People are opinionated based on their backgound, their history in learning and what is the community around them teaching them. Opinions differ in different cultures and regions. In social services the content is rated with these opinions and in basic situations the rating is positive and negative. To wether the content is liked or disliked. 

Hot Topics in feed sorting

This results to the situation when the positively liked content can be shown more often than neutral or negatively rated content by algorithmically sorting the content feed. Basically the same content when released in different context can produce different results in rating. In social networks the context that you choose for your content is relevant. This applies also for in marketing in social networks. 

Rating with Likes

Likes, Up & Down, Loves, Pinning

Social and other services and networks use different names for the same subject of how to rate the content. These names can be likes, upvotes & downvotes, loves, pinning. These methods give the user one point rating to give to the content. The content's total rating is then calculated based on the total points of these ratings.

Voting, Stars, Diamonds

Another system to use in rating is to vote for the content. In this system the user gives content a rating for example in between 1 – 5. This is represented usually in stars in the content rating that is calculated from the average of the total ratings. 

And also one significant point is that the user can give only one rating to each content. This instantly requires multiple features. The user has to be authorized and there has to be a list of the content that the user rates. This list has to be well performing in computing performance. 

In the next chapter we will look in more detail the content rating implementation and how to maintain a list of rated content.

What new ideas or thoughts this chapter gave you?