Streaming services have changed the way in which we experience content. While recommendation systems previously focused on presenting you with content you might want to purchase for later consumption, modern streaming platforms have to focus instead on recommending content you can, and will want to, enjoy in the moment. Since any piece of content is immediately accessible, the streaming model enables new methods of discovery in the form of personalized radios or recommendation playlists, in which the focus is now more on generating sequences of similar songs that go well together. With now over 700 million songs streamed every month, Anghami is the leading music streaming platform in the MENA region. What this also means, is that the amount of data generated by all those streams proves to be an invaluable training set that we can use to teach machine learning models to better understand user tastes, and improve our music recommendations.
Researchers have identified an incredibly smart method used by fruit flies to categorise odours – and it's so clever it could be applied to powering recommendation algorithms for the likes of Netflix or Spotify. In the same way that YouTube might want to flag up videos similar to the one you've just watched, fruit flies – like many other animals – need to know which smells are similar, for finding food and avoiding poisonous substances. The team from the University of California San Diego (UCSD) and the Salk Institute for Biological Studies in California has found that fruit flies have an especially clever way of categorising odours which lets them recognise differences with a very fine level of accuracy. "In the natural world, you're not going to encounter exactly the same odour every time; there's going to be some noise and fluctuation," says one of the researchers, Saket Navlakha from Salk. "But if you smell something that you've previously associated with a behaviour, you need to be able to identify that similarity and recall that behaviour."
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest?
Apple on Monday confirmed it has bought Shazam, the music app that can identify a song by hearing just a snippet of it. The acquisition boosts Apple's position in the music world and advances its artificial intelligence efforts. Shazam, launched in 1999, claims that at least 1 billion people have downloaded its app and used it to identify songs at least 30 billion times. Its service was one of the first AI products to be used by a broad audience. As Apple faces other tech giants in this increasingly competitive arena, analysts say Shazam could add significant value not only with its own service but also by making Apple's AI products -- namely Siri -- smarter about music.
From its first incarnation in 2000, to its online launch in 2005, up through today, Pandora [Music] set-out to differentiate itself -- a music discovery service hand-built on a scientific and proprietary matching engine. In 2000, 80% of the music industry's revenues came from less than 3% of the releases . Tim Westergren, a musician and composer, saw an untapped market opportunity to bridge this gap -- changing the music industry paradigm and dynamics between artists and consumers. Tim saw an opportunity to match undiscovered artists and their music to listeners who would enjoy their sound. Matching would create value for the artists, listeners and the intermediary facilitating this process.
Our vast experience with planning data science conferences across a multitude of industries has enabled us to host, listen and learn valuable insights into the industry's most ambitious goals and research advancements. As the data science community heads towards 2018, we asked our top speakers to comment on 2017's most impactful achievements in Artificial Intelligence and make a few predictions for 2018. We summarize the most notable insights in this post, and offer expert commentary on the advancements, predictions and lessons learned regarding machine learning algorithms and deep learning systems. Daniel Monistere, SVP-Client Solutions at Nielsen points out the technology advancement electronic devices have met and the increase in their storage and data gathering capabilities. Also, applications have become intelligent being able to collect user data.
The recommendation systems at websites such as Amazon and Netflix use a technique called "collaborative filtering." To determine what products a given customer might like, they look for other customers who have assigned similar ratings to a similar range of products, and extrapolate from there. The success of this approach depends vitally on the notion of similarity. Most recommendation systems use a measure called cosine similarity, which seems to work well in practice. Last year, at the Conference on Neural Information Processing Systems, MIT researchers used a new theoretical framework to demonstrate why, indeed, cosine similarity yields such good results.
Personalized experiences are a hot topic these days. Certain types of businesses have become very skilled at delivering personalized service. Think about a hotel you've stayed at before that welcomes you back and remembers that you liked a certain type of pillow, a specific newspaper and a corner room. The experience is becoming more and more common, and this type of service is crossing over into many other industries, especially retail. When a customer walks into a retail store, the salesperson has two choices: simply ring up a purchase, or truly help the customer get what he or she really needs.
Spotify, the largest on-demand music service in the world, has a history of pushing technological boundaries and using big data, artificial intelligence and machine learning to drive success. The digital music company with more than 100 million users has been busy this year enhancing its service and tech capabilities through several acquisitions. Industry watch dogs predict the company will launch an IPO in 2018. When you have tens of millions of people listening to music every minute of the day, you have access to an extraordinary amount of intel that includes what songs get the most play time, to where listeners are tuning in from and even what device they are using to access the service. There's no doubt Spotify is a data-driven company and it uses the data in every part of the organization to drive decisions.
The business headlines are dominated with buzzwords like machine learning, data analytics, and predictive analytics. You hear about case studies like "Chase uses predictive analytics to predict credit worthiness in customers", "Netflix uses machine learning to recommend movies to its subscribers", "Amazon segments customers using AI [artificial intelligence]", and, fantastic as it sounds, "Most hedge funds allow machine learning algorithms to pick and trade stocks… with better success than the experts." Machine learning and artificial intelligence are everywhere. It's pervasive in every aspect of life from you're your big store purchases, to Google search recommendations, to Amazon delivery logistics, to Netflix movie recommendations. It's also everywhere you're not seeing.