Video – The Role of the Recommendation System on Media Websites
Role of the Recommendation System on Media Websites
This video is all about the role of content-based recommendations on media websites. A recommendation engine is an algorithm that suggests relevant items to users based on their likes and preferences. For example, in the case of Netflix, the interface will suggest which movie you should watch based on the flicks that you have already seen; in the case of e-commerce, it is about which product to buy, etc. The recommendation system relies on ML – Machine Learning – an algorithm that depends on AI – Artificial Intelligence utilising Big Data on customer behaviour.
The typical recommendation systems function on the following models:
- Collaborative Filtering – recommending new items to users based on the preferences and interests of similar users.
- Content-Based Filtering – based on the previous content searched for/liked or used by the visitor.
- Demographic-Based Filtering – based on users’ common attributes like gender, location, age, and suggest items based on similar characteristics.
- Utility-Based Filtering – based on the utility function of the decision-maker and recommend high-utility items to users.
- Knowledge-Based Filtering – recommendations based on specific queries made by the user.
- Hybrid Filtering – this technique combines two or more recommendations strategies.
Functions of content-based recommendation
It is prudent for media websites to invest in content recommendation systems as it adds value in many ways. The recommendation engine enhances the customer experience, narrows down their selection options, and suggests naturally tailored content with effective interaction and engagement.
Benefits of using a content strategy through recommendation systems
- It promotes better customer retention and entices them to come back.
- It enhances customer engagement and builds consumer loyalty.
- It augments scalability and boosts sales.
- It improvises recycled content.
Different ways in which media websites can deliver content recommendations
Media websites can create a proficient recommendation system, and they can integrate an effective content strategy using the following methods:
- Suggest the next article, eCommerce suggestion, movie flick, or blog post encouraging them to go through the other content which may interest them.
- Make search more efficient and save time, effort, and inconvenience faced by consumers while sifting through the myriad of content available.
- Allow users to keep a tab on the previously viewed content.
- Enabling the viewers to effortlessly pick up from where they left off by giving them access to recently viewed tabs or personalised space.
- Incorporate vigorous CTAs to highlight recently launched meaningful content and guide the consumers to the website.
Recosense is well-equipped to enable personalisation and customisation across all the digital properties and endpoints through its omnichannel AI-based recommendation and user personalisation platforms. Content-based recommendations on media network systems depend on evaluation metrics offering excellent value-added services to promote user satisfaction and a high level of digital customer experience.