By enabling a level of efficiency and personalization that has never been seen before, machine learning (ML) is enabling the development of user-centric applications. By strategically integrating ML, applications can now anticipate users’ needs rather than just meeting them and proactively adjust to their preferences and behaviors.
The success stories of industry leaders such as Uber, Netflix, TikTok, Google Search, Telegram, LinkedIn, and Revolut demonstrate the impact that machine learning can have on the user experience. These platforms have effectively used ML to scan large data sets and extract insightful data that powers operational optimizations, predictive analytics, and personalized recommendations.
For instance, machine learning algorithms identify patterns in user behavior to precisely forecast delivery times and craft personalized recommendations. This process involves detecting common usage patterns and preferences, leading to the recommendation of items that similar users have shown an affinity for. The approach encompasses three core elements.
3 Elements of Machine Learning Use in App Development
- Understanding Users and Their Contexts: This involves comprehensively analyzing not just the users’ preferences, such as their favored music genres, but also contextual factors like the time of day or their current activities.
- Navigating the Item Space: Machine learning algorithms effectively categorize and understand the vast array of items available, whether songs, products, articles, or services.
- Matching Items to the User’s Context: Finally, the system intelligently suggests items that align well with a specific user’s context and preferences at any given moment.
How to Train Machine Learning for User Preferences
There are three key steps to training machine learning algorithms to adeptly understand and respond to user preferences and behaviors. Here, I will describe them, with the purpose of offering a clear guide that can help you use machine learning to create user-centric applications.
Step 1: Define Your Purpose and Gather Data
If you are not clear about what you are trying to accomplish by implementing ML and about your constraints and requirements, your efforts might be in vain. Therefore, make sure you can accurately answer the question: What does this model need to do?
Based on that, you can define a set of rules and parameters as to how you will evaluate it. For instance, if we are building a model for an e-commerce store, and the objective is to recommend items that are similar to a product that the user was looking at, then we will weigh the accuracy of these recommendations. If the user was buying bread and the model recommended a set of batteries, the model is not working. At the end of the day, the model will have a positive ROI if it results in increased sales because of the quality of the recommendations it provides.
We also need to consider how the model will operate. Using the same example as above, is the model providing recommendations in real-time? Or are these recommendations precomputed? This defines both our inputs and our outputs and guides our data collection process. Also known as the training set, this includes varied examples of user interactions and preferences. This data forms the foundation upon which the algorithm will learn.
To ensure you have the right data, as we discussed, you need to define clear objectives and identify the variables that are relevant to your project. Also, audit the data, as this will screen it for biases or missing values, preventing further inaccuracies.
For example, to build its ML-powered photo classifier, Yelp gathered tens of millions of photos to train its model accurately. This project has helped the company to enhance the navigation experience for visitors.
Step 2: Choose the Right Algorithm
Next, develop a mathematical algorithm. This algorithm’s core function is to generalize common patterns it observes in the training data. It does this by adjusting its mathematical coefficients in a way that minimizes error, effectively ”learning” from the data.
To evaluate for error, we need to split our data set into two parts, which are called the training set and the test set. Then, we adjust ML models coefficients on the training set and apply our trained model on the test data, which is not seen during the training process. Our objective is to measure how far the model predictions were from the real answers. Again, using the previous example, a simple measure would be: How many items that we recommended to users with the test set did they actually buy?
This decides which features — specific data points like the frequency of a user liking an item — we select to feed into the model. The algorithm then processes these features, refining its internal parameters to improve its predictions based on the training set. To maximize the odds of choosing the right algorithm, make sure you have utmost clarity in terms of what you are trying to predict and the patterns that you are aiming to identify.
Here, Airbnb stands out as a success story. The company was able to implement a machine learning model that increased booking conversion by using logistic regression. By incorporating hosts’ preferences, trip characteristics, and travelers’ behavior into the model, and carefully isolating each of these variables to reduce data noise, Airbnb effectively created more successful matches between guests and hosts, improving the overall user experience.
Step 3: Test the Model
Once it receives a new request to predict a user’s preference or behavior, the model applies its learned formula to the provided features. It then generates a prediction, offering the most probable response based on the patterns it has recognized from the training data. At this point, you can evaluate the results that you get and see whether the machine learning algorithm fulfilled its purpose. This is the moment of truth: Is our model doing what we wanted it to do? To compare, create a “golden set” of examples that you can use to test new models or improved versions.
For instance, if you’re the ML team lead at Spotify, you would measure whether the recommended songs or playlists increased user engagement to determine if your model produced the desired results. If the results aren’t what you expect, don’t be afraid to pivot.
Challenges of User-Centric Machine Learning
When developers set out to incorporate machine learning into user-centric applications, they face a number of obstacles and factors that they must carefully consider to ensure a successful implementation. The cold-start problem, which means that there is little to no information about a user, makes engaging with new users or those who don’t have an extensive interaction history difficult. This creates a dilemma since predicting or suggesting customized experiences in the absence of significant user data becomes difficult.
The complexity of models introduces an additional challenge that requires specific expertise and resources for efficient development and application. Because of this complexity, we need to ensure that the model is aligned with the users’ expectations, as otherwise, we will be wasting resources and fail to make the user experience better for our customers.
Conceptual obstacles are not the only ones on the path to machine learning implementation, however. Training and maintaining these models requires considerable financial outlay, time commitments, and computational resources. To maintain accuracy over time, your ML team must continuously work on model maintenance and accuracy measurement. Even in advanced machine learning, interpretability, always an elusive component, remains so, which makes comprehending and justifying the predictions’ reasoning difficult. Lastly, you can’t ignore data quality, the cornerstone of efficient machine learning. As a Harvard study said, “If your data is bad, your machine learning tools are useless.”
Therefore, data needs to be audited to account for any potential biases and inaccuracies. Otherwise, the model will perpetuate these biases and not accomplish its objective.
Ethical Considerations in User-Centric Machine Learning
In addition to technical complexity, you also need to address ethical considerations. In terms of data security and privacy, your company must protect user information. This includes processing, storing, and gathering data securely in accordance with regulations such as the GDPR, as well as openly informing users of the reason for collection and the use of any data that you gather.
Fairness and bias are also important factors in machine learning. Unfair outcomes may result from the unintentional perpetuation of biases in training data. By addressing biases in data and algorithms, you ensure that all users are treated ethically, promote fairness, and reduce the likelihood of discrimination.
In order to build user trust, transparency and explainability are crucial. Users are entitled to know the effects of decisions made using machine learning, and those implementing ML need to face accountability for their actions.
Last but not least, user feedback plays a crucial role in refining machine learning models for user-centric applications. Active user input is indispensable for continuous improvement and addressing evolving user needs. Balancing user privacy and data collection involves obtaining explicit consent, transparently explaining data usage, and carefully managing the delicate trade-off between privacy and the necessity of collecting data for machine learning applications.
Machine Learning Improves Your Development Process
By facilitating hyper-personalization, machine learning has the capacity to completely transform the way companies interact with customers. Also, if you harness this capacity the right way, companies can substantially increase operational efficiency, and like Airbnb did, improve their conversion rates by bolstering the user experience. To do this, there are three steps that you can follow to ensure that your ML implementation has the highest odds of success.
By gathering a robust data set, choosing the right algorithm, testing the model, and pivoting when necessary, you can maximize the chances that a machine learning implementation works in your favor, and that you can obtain a positive return on investment. But you need to consider the relevant technical, financial, and ethical challenges that come with integrating ML into your business. Underestimating these can lead to your project’s failure, and ultimately, to the demise of your business.