AI is All About Right Content in Internet Business

Primarily four types of content are available on the internet: text, images, audio, and video. Guess what most internet-based business firms are primarily offering to their customers in the first transaction: it is essentially the content. So where does AI come into the picture? These are the recommendation engines and systems of artificial intelligence which are delivering such content based on various parameters, features, and filters.?

Here is a very fine example of a food delivery app. I just wanted to order some food online. As I signed up it asked for my location access and the address. I have granted the location access and provided the other details. The app immediately started recommending the nearby restaurants open for the order. The AI-based system that worked on the content was the demography-based recommendation and the type of content was the text and images.

I placed my order and tried to contact the restaurant for cooking instructions. There, an audio and a text boat came for assistance on their mobile app. When I was waiting for the food delivery, the app portal recommended some food-related short video-based content. The whole AI that worked behind it was a content-based recommendation engine. Image and text-based content helped me order the desired food. Audio and video-based content helped me to get more information and engagement on their app.

So, internet-based firms and startups such as Facebook, Google, Netflix, Twitter, OYO, and Swiggy are basically employing most of their AI efforts on the content recommendation. And for the same, they are creating and inventing more and more content. The startups claiming to be AI-based are working for more automation of content recommendation. The content-based recommendation systems are one of the kinds of content suggestion systems that are working in the background of these platforms.?

The other major types of AI recommendation engine that work on content recommendation are Collaborative Filtering. It maps similar consumer behaviour and demographic data to predict and recommend the data. Collaborative Filtering is also of two types:1) memory-based, 2) model-based. The memory-based content recommendation works on the user and item similarities whereas the model-based recommendation works on the various statistical models of artificial intelligence. E-commerce internet businesses are prime examples of collaborative filtering AI recommendation engines.

Of course, all internet-based firms need a lot of content to offer such automation. I have personally worked on two such automation projects. One was with my current employer developing an AI recommendation system and another is building a huge data stack for the firm to develop the industry-specific AI search engine. Hence if you are in the internet business, your AI automation should be first focused on the right content recommendation. If you are not able to do that, please reach out to me for a free AI, automation, and process excellence consultation.