The pioneer of an immortal technological invention, Steve Jobs once said, “A lot of times, people don’t know what they want until you show it to them.”
Your video streaming channel might have a rich content library, but the viewers will not know that until you bring them in the front row. And the best and most accurate way to do that is by recommending personalized content to your users.
It is a harsh truth that consumers are utterly impatient. So, if you can help them reduce the time they spend in searching for their favorite content, they will remain loyal to your channel for sure.
And so forth, what the viewers will need is a virtual friend who will suggest videos most accurately by analyzing past viewing patterns, preferences, etc.
This virtual friend is the recommendation system that must get integrated into your platform for increased user satisfaction. Netflix is a live example.
It actually works much like the word-of-mouth recommendations that we get from friends, which surely eases our decision-making process.
Alie – The Best Content Recommender for streaming services
Now, you must be curious to know how it is happening! How recommendation system can understand human behavior and suggest content on that basis! It is the machine which learns the patterns (Machine Learning) with the help of its intelligence (Artificial Intelligence).
Alie, being the smartest recommendation system, uses its machine learning algorithm in the perfect manner to offer your consumers with the most accurate suggestions.
Here is a simple presentation of how Alie works:
Now let’s dig into each one of them to understand why Alie is the ideal fit for your Video Streaming Service:
Being the first step of the flow, gathering the user data builds the base of how well a recommendation system can work. More data the engine will have, more accurate will be the recommendation. And that’s where Alie gains the momentum.
It is designed to collect both explicit and implicit data from the users. Explicit data are those which are directly provided by the users such as ratings, comments, preferred genre & language of movie/TV show, etc. On the other hand, implicit data are those which Alie creates by itself with the browsing history, click-throughs, watch list events, and so on, of each user.
As every single user has its own taste, every data set is also distinct in their own sense. And this collection leads to personalized recommendations for each of the viewers.
The storage space should always be a well-managed one where any data can be stored securely. With the usage of the highly scalable cloud-based database, MongoDB, Alie has simplified and secured data processing. That’s why it can concentrate more on real-time recommendations.
P.S. Keep on reading to know more about real-time recommendations.
By now, the recommender system has collected the data and has stored into the database. What next? How the engine will start recommending content to your viewers?
The data needs to be analyzed properly so that the system can filter out similar data leading to precise recommendations. Alie does the same in real-time using AI which has made it the smartest recommendation engine.
What is Real-Time Recommendation System?
This process lets the recommender system to process the data as it is created. This system allows Alie to generate recommendations immediately to your viewers, just the way Netflix does for its viewers. Your viewers will not have to wait for the recommendations to pop up in front of their screen, it will be ready by the end of content, or maybe in-between, if they want to skip.
It is the final step where the necessary data will be filtered from the entire database and only the relevant ones will be shown to your viewers. Here are 4 methods that empower Alie by providing the most accurate recommendations to your viewers:
- Content-Based Filtering: It works based on the meta-data of your content – it’s title, genre, language, and others along with the user ratings and comments.
Let’s take a broad example, if a viewer has liked Avengers series, it is likely that (s)he will also like Iron Man, Captain America, or any other Avenger movie.
- Collaborative Filtering: This is where the machine learning algorithm gathers a huge amount of data based on users’ behaviors, preferences, and activities. And then it recommends the content to those having similar choices.
For example, Person A likes the movies x, y, and z; and Person B likes the movies w, x and y. In this scenario, the machine learning algorithm will predict that A will also like w and B will also like z because of their similar liking towards x & y.
- Hybrid Filtering: This method is a collaboration of both of the above-mentioned filtering systems. It recommends content to the viewers based on their own likings (content-based) and the comparison between the habits of different viewers.
- Time-Based Filtering: The recommendations are made based on user behavior during a particular time of the day.
For example, One watches FRIENDS on every weekend, then Alie will recommend similar TV series to your viewer during the weekend.
According to a recent survey, 8 out of 10 viewers prefer watching recommended content when they get an accurate result. Consumers now consider personalization as a privilege and greatly depends on content recommendations.
In such scenarios where competition in the OTT industry is increasing, it is wise to integrate a recommendation engine to ease the content discovery for subscribers. Once users find it easy to choose the content of their interest without wasting more time in browsing, for sure they are going to return and will stay loyal with the streaming service.
When Alie is made here ready to fit perfectly with your platform, you would not have to look somewhere else for building a recommendation engine. It can also be custom-made to cater to any of your needs. It will help you to drive more users to your platform & more views to your content as well.