Targeted Advertising in the Online World - Recommender Systems

Targeted Advertising in the Online World - Recommender Systems ads

One of the most important factors in the success of this business is the ability to correctly match the right content to each user profile. Multiple campaigns can be running at the same time and the system needs to decide which advert should be shown, based on a variety of real-time signals, such as user location or device type.

Recommender systems are widely used by many companies to recommend items and advertisements to their customers (e.g., Amazon and Netflix). However, building accurate recommender systems for large-scale products remains challenging since very little training data is available for many users (cold start problem). In this blog we will briefly introduce how recommender systems work and then focus on two problems in our industry: ranking and personalization. We will cover some practical aspects that are important for building scalable recommender systems in operational environments such as continuous learning and serving at scale

Online advertising has become a key revenue model for online services, such as search engine and video streaming. The online advertisement business is expected to reach $335B this year, of which almost half ($159B) are digital adverts.

Online advertising has become a key revenue model for online services, such as search engines and video streaming. The online advertisement business is expected to reach $335B this year, of which almost half ($159B) are digital adverts.

The online ad market can be split into two categories: digital and non-digital. Digital ads include banner ads, sponsored links and native advertising (advertisements that look like the content surrounding them). Non-digital ads include TV commercials, radio advertisements and print advertisements. In 2015, the non-digital segment accounted for 85 percent of all spending on Internet advertising but its share is expected to decrease over time as more companies invest in digital marketing strategies.

One of the most important factors in the success of this business is the ability to correctly match the right content to each user profile.

One of the most important factors in the success of this business is the ability to correctly match the right content to each user profile. The data that comes from users and other sources (browsing history, search queries etc.) is used by recommendation systems to deliver a better experience for users by tailoring content according to their preferences. This can be done through personalised recommendations or through content curation based on a wide variety of factors like location, subject, genre etc.

Multiple campaigns can be running at the same time and the system needs to decide which advert should be shown, based on a variety of real-time signals, such as user location or device type.

The system needs to be able to decide which advert should be shown, based on a variety of real-time signals, such as user location or device type. For example, if the user is in London and on their laptop then they are more likely to see an advert for the latest version of Windows 10 than if they were using their phone with cellular data turned off.

The system can only make this decision in real-time if it has access to all relevant data about each individual user so that it can make recommendations instantly.

Conclusion

We hope you enjoyed reading this blog and it gave you an insight into how the Recommender System Ads market is so important and increasingly prevalent in day to day life. Cubiq are partnered and are actively building multiple AI team teams focused on Recommender Systems, Personalisation and Ranking in the Ads space so if you’re looking for talent or looking for a new opportunity in the space – get in touch!