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Music Recommendation App
Due to AI, music recommendation app which can also be known as music recommendation engines makes it quicker and easier to show music recommendations that are tailored to each user’s interests and preferences.
So how does this work? First, it collects all the data: what are the songs that the users listen to the most, what is the genre of the song, and which language is the song the user listens. Next, it stores all these data and analyses. It then recommended songs from a similar genre and the same language and the songs whose ratings are high. You would have seen this in apps like Spotify or wynk, where they have an entire section on songs recommended for you. So they use AI to make this recommendation engine. You can program this music recommendation app by learning from some online blogs or watching YouTube videos.
ou open your favorite streaming app, and instead of drowning in the endless music catalogs, immediately turn on ready-made playlists with songs tailored to your specific tastes. This all is possible thanks to AI-based music recommendation systems (MRS).
In this article, we will survey such recommendation systems for music that are especially used in streaming-like music & audio services. We are going to learn the basic principles of its work from both technical and user points of view, look into its benefits and business use cases, supporting them with a real example.
For the icing on the cake, we will create a collaborative music recommendation engine by ourselves: for this, we will use a free public dataset and apply the K-Nearest Neighbors (kNN) algorithm to identify songs that you like.
This article will be useful for startups in the Music & Audio industry, especially for those whose business model revolves around streaming technologies. And if you are just an enthusiast curious about how music recommender works and probably even willing to test it in practice – keep on reading to satisfy your curiosity.
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Recommendation system is a filtering system, the purpose of which is to predict the preference that a user would give to a particular element, in our case – to a song. It is a core of huge engines that work by certain recommender algorithms and suggest a single item or a set of items to users based on such predictions.
Whether we are aware of it or not, a variety of recommendation systems have become an integral part of our daily routine since recently. Starting from accurately targeted advertising product suggestions and finishing with personalized video or music playlists compiled specifically for us – album recommendation engines seem to be encompassing our everyday lives from literally every corner of digital space.
A phenomenon of these days, TikTok is built all around the song recommendation engine: that’s why TikTok’s algorithms are considered to be unique and are promising many more opportunities to the creators to grow organically - or in other words, with the help of recommender system algorithms.
In the music industry, recommendation systems are part of a big engine of streaming apps like Spotify, YouTube Music, Deezer, Tidal, and the like.
They serve to ensure a quality streaming experience for you.
There are 2 most popular recommendation systems:
The content-based approach relies on the similarity of particular items. While using a streaming music service, a user puts likes or dislikes on the songs, creating playlists. The main idea of a content-based recommendation system is to extract keywords from the description of a song that a user likes, compare them with the keywords from the other songs, and, based on this, recommend similar songs to the user.
In turn, a collaborative system is built on the basis of users’ overlapping preferences and ratings of songs. It assumes that if user A and user B express similar preferences, similar songs can be recommended to them, meaning that if user A likes a particular song, it is likely that this song will also appeal to user B and vice versa. Collaborative recommendation systems are generally considered more accurate, as they rely on direct user interactions with the system versus content similarity.
You might wonder, how does this process look from the user’s point of view?
As we have already mentioned, these recommendation systems do not require any additional actions on the user's side. A person simply installs the application and registers, listens to his favorite songs, makes playlists... And the more data we have about his preferences, the more accurate our song recommendations to him become. It means that as the accuracy of algorithms will grow, a user will enjoy music and will be satisfied with a music service even more.
People nowadays are very used to recommendation systems in all areas: news feeds, streaming video and audio services, online stores. For a music service, a recommendation system is a must, given the current consumption habits in our modern world.
Music subscription services use recommendation systems to shape individual songs into comprehensive playlists by particular criteria. That’s for the convenience of the user and to increase customer satisfaction.
Platforms that sell music tracks or albums use recommendation systems to increase sales. It works by the following scheme: when a user buys a certain track, a recommendation system suggests some additional similar tracks for him to buy, in such a way encouraging him to make more purchases.
You need a Music Recommendation System if you want to:
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The rise of streaming apps and the implementation of music recommendation systems have affected users’ habits in consuming music. According to almost 75% of user’s experience is tightly interwoven with playlisting.
Apart from ready-made playlists that listeners make use of, there is also a growing trend of creating your own playlists and sharing them with your friends. However, this is getting more and more challenging due to the immense number of tracks online – a so-called “choice overload”.
To make it easier, streaming services like Spotify and Apple Music have offered their listeners to also use automatically generated song suggestions while making a playlist by themselves. In this case, AI-algorithms can recommend tracks based on the name of a playlist or some of the tracks already included by a user.
A Swedish company Spotify is now viewed as a king in the music industry since streaming services have become one of the main revenue streams to artists and labels. The value of implementing a recommendation system in the service cannot be overlooked, as it has had a massive impact not only on Spotify business but also on the whole music industry.
Firstly, the algorithms recommending music based on listening experience rather than top artists have helped to handle, to some extent, the issue of uneven royalty payment distribution. Before MRS, listeners would give preference to listening to the famous top artists and songs. Consequently, emerging artists didn’t really have a chance to be discovered. With the help of MRS, Spotify has reorganized music around user preferences, mood, and feelings, which has opened new opportunities to both industry players and listeners.
The second important point is that by gathering detailed data about listeners’ habits, Spotify can sell this data to advertisers. It is of great value to the whole market participants, including labels licensing music to the streaming service. The data provides the industry with insights into listeners' habits, giving an understanding of how they consume music and what else they like. This huge privilege has granted Spotify a superior position in the whole music industry.
Spotify now has over 100 million users. For each of them, a fresh "Discover Weekly" playlist arrives on Monday. Spotify is considered a leading streaming service, and its song recommendation algorithm can boast of a high rate of accuracy.
For you to take a glimpse into how one of the best music recommendation systems works and what is the key to such accurate and personalized playlists, let’s learn more about Spotify music recommendation algorithm.
In fact, Spotify did not invent a new algorithm but rather combined 3 of the best ones that are used in this area. The result is a powerful unique mechanism that makes a very accurate list of recommendations.
The service uses 3 models to create playlists with recommendations:
We have already discussed the method of a collaborative model. Let’s now take a closer look at NLP and audio models.
The NLP model of Spotify is constantly searching on the Internet for articles about songwriters and songs. After collecting the information, the model uses the descriptions and analyzes their content to construct the vector. Comparing vectors allows us to determine the similarity of two or more songs.
But let’s consider the case when a beginning artist uploads his song to the service. In this scenario, the first 2 models will not show this song in the recommended lists since there is little or no data about it on the network, and it will not be included in the collaborative filtering sample.
This is where the third model comes to the rescue: it analyzes the song using a neural convolutional network and creates a spectrogram. After passing through the network, the algorithm knows the characteristics of the song — its volume, timbre, size, harmony, etc. Then, it compares them with the characteristics of other songs and finds similar ones, after which the track goes to the playlist "Discover Weekly".
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