Binge-watching is the new vogue for lockdown-stricken internet users. The behavioral shift is exciting as well as challenging OTT (Over-the-Top) media services to satiate growing demands with quality content. Matching the taste of millions of users across geographics requires data-driven capabilities and Artificial Intelligence (AI). Under the hood, recommendation engines for OTT platforms work as an adaptive content delivery and analytics system that drives engagement.
We, at Oodles, a Machine Learning Development Company, explore why and how AI-driven recommendation systems contribute to OTT’s profitability and success.
The expanse of OTT and VOD (Video on Demand) services is continuously widening to reach millions of customers. The internet’s availability to more remote corners of the world is adding up to OTT’s vast customer base. Recommendation engines for OTT platforms are essential to match the dynamic needs of a wider audience with quality and seamless content.
Calling recommendations the secret sauce for OTT businesses, Reed Hastings, co-founder of Netflix, quoted,
“If the Starbucks secret is a smile when you get your latte… ours is that the Web site adapts to the individual’s taste.”
Not only Netflix but other leading VOD services including subscription-based, transactional, ad-supported, and hybrid platforms use recommendations to fuel customer retention.
A bifurcation of OTT services by Therese Moriarty
The objective of powering OTT platforms with recommendations is to personalize the online streaming experience for every individual viewer. Personalization not only increases site stickiness but also improves customer loyalty with millions of customers wanting to rewatch popular series religiously.
Data is the powerhouse of any recommendation system. However, human capabilities don’t suffice to make sense of the data explosion, thanks to the information age. It is with the advent of artificial intelligence services that businesses can analyze and extract actionable insights from huge datasets.
Especially for VOD, AI-driven recommendation engines parse through minute and hidden factors in customer data to understand their choices better.
Customer data used by OTT recommendation engines includes a range of parameters divided as below-
AI’s underlying machine learning algorithms can channelize diverse datasets to generate recommendations based on viewers’ history, preferences, content type, etc.
Linear regression, information retrieval, and personal video ranker are some effective machine learning algorithms used to build OTT recommendation systems. These algorithms involve varied dimensions of user data that eventually affects the outcomes from demographic to film-oriented to personalized recommendations.
In addition to personalization, here are other AI capabilities contributing to the success of recommendation engines for OTT platforms-
A growing customer base is the most profitable yet challenging factor that demands robust software architecture and strategies to improve scalability. Implementation of collaborative filtering and matrix factorization algorithms scale OTT recommendations to support a thousand customers to a million without much hassle.
The considerable reduction in the cost of computational powers across on-premise and cloud solutions are encouraging businesses to adopt AI. Powerful tools like Apache Hadoop and Spark are efficient tools to deploy recommendation systems. They enable OTT businesses to collect and analyze multidimensional data from social media, review and opinion pages, feedback portals, and more.
Proclaimed as the largest open-source framework for data processing, Spark is indeed a powerhouse for processing enormous data. Spark is the first choice for implementing machine learning and big data analytics, providing elegant APIs for analyzing distributed data. For OTT recommendations, Spark can facilitate the much-needed scalability while promising speed and ease of use across business models.
Here’s an architecture of a linear regression model involving Kafka for stream processing, Cassandra for database management, and Spark for recommendations.
Another project of Apache Foundation, Mahout is a dedicated framework for creating and implementing scalable machine learning techniques. Mahout is best suited for co-occurrence type of recommendation systems that analyze content that appeared together in user histories. The identified items are used to trigger further recommendations based on customer segmentation and other parameters.
Hadoop is yet another data processing framework that uses a pool of connected computers for solving computational problems. Together, Spark and Hadoop can analyze big data from disparate sources to implement collaborative filtering models similar to Amazon recommendations.
The momentous rise of OTT and VOD adoption, especially amid lockdown, has widened the horizon of AI technology for real-world applications. We, at Oodles AI, are a team of seasoned AI developers and software architects building enterprise-grade AI solutions.
Our capabilities under recommendation systems expand to-
a) Custom recommender systems
b) Cloud-based recommender systems
c) Knowledge graph-based recommendation systems, and
d) Reinforcement learning
We deploy frameworks within the Apache suite including Spark, Mahout, Cassandra, Kafka, and Hadoop to strengthen recommendation engine development for OTT, eCommerce, online bookstores, and other businesses.
Collaborate with our AI Development team to boost the growth and sales of your online businesses using AI technologies.