Content is king and will rightly remain so. However, recently the flow of content has seemingly increased , and it’s been difficult to keep track of it. That’s where market experts have backed artificial intelligence (AI) to play a key role, and this is believed to be a game changer in the way consumers view content on video streaming platforms. “I believe the integration of AI in content recommendation systems can enhance the accuracy and personalisation of content suggestions. By analysing user behaviour, preferences, and interaction history, AI algorithms can predict and recommend content that is tailored to individual tastes,” Deepika Loganathan, co-founder and CEO, Haive, an artificial intelligence (AI) startup, told FE TransformX.
Behind the scenes
The basic understanding of AI-based content recommendation systems is that they make use of different software to provide content-based advice to users. From what it’s understood, the content could range from videos to articles, music, among others. Upon an interaction between the user and the content platform, the mechanism is able to detect a pattern of the user’s behaviour and then provide content suggestions belonging to that search category. So, where does AI fit in this landscape? Well, the benefits associated with AI include data processing related to search history, user’s needs and wants, and decisions related to the content relevance. Furthermore, AI in content recommendation mechanisms can be utilised for calculating user-based data at faster rates, catering to the needs of a mass user based, and bettering its suggestions over time through user-oriented evaluation.
Now, talking about the different AI-based content recommendation systems, there seems to be five to six main types, namely knowledge-based recommendation systems, which make use of graphs to help mainly e-commerce platforms, reinforcement learning recommendation systems, which make use of machine learning (ML) programs, contextual recommendation systems, which provides suggestions based on time and location, collaborative filtering, which channels data belonging to many users, content-based filtering, which is based on the kind of content consumed, and hybrid recommendation systems, which is an amalgamation between content-based and collaborative filtering.
“I believe that the advent of AI-oriented content recommendations has transformed how we engage with digital content. This personalised touch seems to enhance the user experience and keeps one engaged with the platform. These intelligent recommendations can lead one to explore a wide range of topics and genres. AI not only aims to keep users engaged but also helps platforms maximise their revenue potential,” Somdutta Singh, author and advisor – Government of India, specified.
Market players, and its overview!
It is believed that Netflix makes use of this structure for different purposes, such as creating thumbnails based on a watched movie or television (TV) program, predicting the number of subscribers it’ll have in the future. Data from Statista, a data and business intelligence platform, revealed that approximately 220.67 million global users tune in to Netflix monthly. Data published by Wired, an American magazine, mentioned that the total annual cost pertaining to Netflix’s recommendation structure sums up to nearly $1 million, which can be correlated with the company’s financing of areas such as data science and ML. “By employing AI in content recommendation systems, companies can enhance user retention and satisfaction, which in turn can drive subscription renewals and attract new customers. Additionally, AI can optimise content curation and even influence content creation based on insights derived from user behaviour and preferences,” Loganathan added.
According to McKinsey & Company, a management consulting firm, 75% of Netflix users’ consumed content is based on its AI-based content recommendation system. Reportedly, a paper called “The Netflix Recommender System: Algorithms, Business Value, and Innovation,” published by Netflix, revealed that the AI-based content recommendation system helps the company save up to roughly $1 one billion on an annual basis. Even Spotify has stated that the execution of a new recommendation mechanism resulted in an upward trend in their month-to-month customer base count, which increased from 75 million to 100 million. Sources suggest that Spotify makes use of AI to develop song recommendations and create personalised playlists, while YouTube makes use of it to recommend videos adhering to a user’s desires. MIT Technology Review, a bimonthly magazine, stated that YouTube’s content recommendation system supports 70% of what people consume on the platform.
In accordance with Mordor Intelligence, a market intelligence firm, the global recommendation engine market size has been estimated to clock $5.17 billion in 2023 and should reach $21.57 billion by 2028, at a 33.06% compound annual growth rate (CAGR) between 2023-28. The firm also mentioned that the growth in need for recommendation engines can be correlated with the amalgamation between ML models and AI-oriented cloud platforms, as it can increase automation across different sectors. Moreover, it has been predicted that AI-based content recommendation systems will get advanced, upon their introduction to augmented reality (AR) and virtual reality (VR) technologies. “With the support of other tools, such as AR, a number of things can happen in terms of getting users new stuff to watch and read. Looking into this future, it envisions a time when finding new content should be exciting, just like consuming the same,” Hariom Seth, founder, Tagglabs, an AI-based company, concluded.
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