Preamble by Kenneth Norrgård
As the author´s supervisor, I am glad that she chose to write this article. During the process, I have learned to appreciate her ambition and forward-thinking attitude. The thesis shows that she can conduct research and document her findings. She can make syntheses and draw conclusions based on theoretical references. I do recommend that anyone who is interested in machine learning and personalization read this article. It will show a small portion of the work and if you want to know more, the thesis can be downloaded at VAMK Theseus.
Machine learning has revolutionized the way businesses personalize their products and services to customers. Personalization has become a crucial aspect of customer experience and customer loyalty. With the use of machine learning, businesses can provide personalized experiences to customers, predict their preferences, and offer recommendations that resonate with their interests. In this article, I will be exploring the role of machine learning in personalization, and how it is being used across different industries.
Personalization refers to the practice of tailoring products, services, and experiences to the needs and preferences of individual customers. Personalization has become a critical aspect of customer experience and customer loyalty. Customers now expect personalized experiences and are more likely to engage with brands that provide them.
Machine learning is a subset of artificial intelligence that uses algorithms and statistical models to enable machines to learn from data and improve their performance without being explicitly programmed (Goldenberg et al., 2021). Machine learning algorithms use patterns in data to make predictions and identify relationships between variables.
Machine Learning and Personalization
Machine learning plays a critical role in personalization. It enables businesses to analyze massive amounts of data in order to gain insights into customer behavior and preferences. Machine learning algorithms use these insights to provide personalized recommendations and experiences to customers. Machine learning is being used in personalization across different industries, including:
E-commerce: E-commerce platforms use machine learning to provide personalized product recommendations, offers, and support to customers (Lemon & Verhoef, 2016).
Healthcare: Healthcare providers use machine learning to provide personalized treatment plans and recommendations to patients (Ting et al., 2018).
Education: By analyzing data on student performance, machine learning algorithms can provide insights that help teachers create customized lesson plans and adapt their teaching strategies to meet the needs of each student (Jando et al., 2017).
Media: Media companies use machine learning to provide personalized content recommendations and advertising to customers (Chandra et al., 2022).
How Spotify Uses Machine Learning and Personalization
Spotify is a music streaming service that uses personalization and machine learning to provide its users with a personalized listening experience. This means that Spotify uses the information from the user’s listening history, as well as other data points, such as the user’s location, to recommend music that the user is likely to enjoy.
One way that Spotify uses personalization and machine learning is through its “Discover Weekly” playlist. This playlist is created for each user every week and is tailored to their individual listening habits. Spotify’s machine learning algorithms analyze a user’s listening history and compare it to other users with similar tastes to create a playlist of songs that the user is likely to enjoy. Another way that Spotify uses personalization and machine learning is through its “Daily Mix” playlists. These playlists are also tailored to each user’s individual listening habits, but are designed to provide a mix of songs that the user already knows and loves, as well as new songs that they may be interested in (BCA, 2018).
Spotify also uses personalization and machine learning to recommend new music to its users. When a user listens to a song, Spotify’s algorithms analyze the characteristics of that song, such as its tempo, key, and mood, and use that information to recommend other songs that are similar (Kaput, 2022). Spotify’s use of personalization and machine learning helps to create a more enjoyable listening experience for its users by providing them with personalized recommendations that are tailored to their individual tastes and preferences.
Spotify’s Personalization and Personalized Healthcare
The personalization algorithms used by Spotify can be applied to personalized healthcare in a number of ways (Leff & Yang, 2015; Sanchez et al., 2022; Singh et al., 2022). Some of them are:
Personalized treatment recommendations: Just as Spotify uses data about a user’s listening habits to recommend music, healthcare providers could use data about a patient’s health history and genetic makeup to recommend personalized treatment options that are tailored to their individual needs.
Disease prediction and prevention: Spotify’s algorithms use data to predict what songs a user is likely to enjoy, and healthcare providers could use similar algorithms to predict which patients are at risk for certain diseases based on their health history and other factors. This could allow for earlier detection and prevention of diseases.
Personalized health coaching: Spotify’s “Daily Mix” playlists provide a mix of songs that the user already knows and loves, as well as new songs that they may be interested in. Healthcare providers could use a similar approach to provide personalized health coaching that is tailored to the patient’s individual needs and preferences.
Remote monitoring: Spotify’s algorithms use data to provide personalized recommendations to users, regardless of their location. Similarly, healthcare providers could use remote monitoring devices to collect data about a patient’s health status and use personalized algorithms to make recommendations based on that data.
Age-related Macular Degeneration Treatment Using Machine Learning and Personalization
Age-related macular degeneration (AMD) is a common eye disease that can cause severe vision loss in older adults. While there is no cure for AMD, treatment options exist that can slow its progression and preserve the patient’s vision. Machine learning and personalization can be used to optimize treatment strategies for individuals with AMD (Schmidt-Erfurth, & Waldstein, 2016).
Diagnosis: AMD can be diagnosed through a comprehensive eye exam that includes a visual acuity test, dilated eye exam, and imaging tests such as optical coherence tomography and fluorescein angiography. Machine learning algorithms can be trained on large datasets of these tests to accurately diagnose AMD and differentiate it from other eye diseases (Grassmann et al., 2018).
Disease progression monitoring: AMD can progress at different rates in different individuals. Machine learning can be used to develop algorithms that can predict the rate of progression of AMD based on various factors such as age, genetic risk factors, lifestyle habits, and imaging tests. This can help personalize treatment strategies for each individual and optimize their outcomes.
Personalized treatment: Current treatment options for AMD include anti-VEGF injections, photodynamic therapy, and laser therapy. Machine learning can be used to predict which treatment will be most effective for an individual based on their disease characteristics and other factors. Personalization of treatment can also involve optimizing dosing regimens and treatment intervals to reduce the burden of treatment on the patient while still maintaining the effectiveness of the treatment (Burlina et al., 2017).
Remote monitoring: In some cases, it may be difficult for individuals with AMD to regularly attend in-person appointments with their ophthalmologist. Machine learning can be used to develop algorithms that can remotely monitor AMD progression and detect any signs of worsening vision. This can allow for early intervention and personalized treatment adjustments.
This can also be visualized by the following flowchart:
Overall, machine learning and personalization can help optimize the diagnosis, monitoring, and treatment of AMD. By tailoring treatment strategies to each individual, we can improve outcomes and preserve vision in those with AMD. However, it is important to note that machine learning algorithms should be validated through rigorous testing and clinical trials before being used in routine clinical practice.
Machine learning has revolutionized the way businesses personalize their products and services to customers. Personalization has become a critical aspect of customer experience and customer loyalty. With the use of machine learning, businesses can provide personalized experiences to customers, predict their preferences, and offer recommendations that resonate with their interests. Machine learning is being used in personalization across different industries, including e-commerce, healthcare, finance, and media.
Personalization has important implications for businesses, as it allows them to create more engaging, relevant experiences for their customers, which can lead to increased engagement, loyalty, and ultimately, revenue. However, it’s important to note that personalization also raises important ethical and privacy concerns, as businesses must ensure that they are using customer data in a responsible and transparent manner.
The personalization algorithms used by Spotify could be adapted to personalized healthcare to improve patient outcomes by providing tailored treatment options, predicting and preventing diseases, providing personalized coaching, and enabling remote monitoring. However, it’s important to note that any healthcare-related applications of these algorithms would need to comply with strict privacy and security regulations to protect patients’ sensitive health information.
Machine learning is a powerful tool for personalization, but it’s important for businesses to approach it with care and consideration for their customers’ privacy and well-being.
This article is based on the thesis work done by Ritika Giridhar, and supervised by Kenneth Norrgård, MSc. The thesis permalink is https://urn.fi/URN:NBN:fi:amk-202304195592.