How to Build a Machine Learning Recommendation System
Machine learning recommendation system is a type of software that uses machine learning algorithms to make recommendations to customers. It helps in improving the customer experience and also in increasing sales for retailers.
It employs matrix factorization and other dimensionality reduction techniques to decompose the original user-item rating matrix into lower-dimensional representations called latent factors.
One of the most popular recommendation models, collaborative filtering gathers data from past user interactions with items like songs, products, movies and videos. It then uses this information to identify similar users and recommends items that those users may have interacted with or shown interest in.
A good example is a website that sells sports equipment, where collaborative filtering can identify John and Sarah as users with similar interests. So, the website can recommend that Sarah buys a particular basketball.
Unlike model-based algorithms, collaborative filtering doesn’t require features to be known about the items or users such as genres or writers. This makes it a suitable option for recommendation systems that need to be applied to a broad range of products such as e-commerce platforms.
To improve the accuracy of collaborative filtering, dimensionality reduction techniques such as matrix factorization can be used to decompose the user-item interaction matrix into latent factors. This reduces the complexity of the original data and allows for faster computation of recommendations.
Matrix factorization is one of the most commonly used techniques in machine learning recommendation systems. It decomposes a high-dimensional matrix Xmxn into a product of lower-dimensionality matrices, which are easier to compute and store. It also reduces the noise in the data and reveals latent factors. This makes it easier for training algorithms (such as random forest and gradient boosting) to learn from compact information-dense features.
A typical model uses a combination of matrix factorization and deep neural networks. The neural network learns rich representations of relationships in the data and can generalize them to similar items via its embeddings. However, it requires a large number of examples to train well.
Matrix factorization, on the other hand, takes a linear perspective on the problem and can reduce the number of examples needed to train a model to competitive accuracy. Moreover, it does not require explicit numerical ratings. The resulting models are often more stable and can be deployed in real-time.
When building a machine learning recommendation system, a scalable and managed database is essential to avoid unnecessary tasks. Depending on the application, data can be stored in a standard SQL database, NoSQL database, cloud data warehouse or a data lake. The choice of the storage depends on the size of the data and how quickly recommendations need to be generated.
In order to achieve a high level of accuracy and efficiency, it’s recommended that machine learning algorithms be accelerated with GPUs. Many of the mathematical operations in these models are matrix multiplications which can be significantly accelerated with GPUs.
Recommender systems have already been used to boost engagement and sales for popular platforms like Netflix and Amazon. By leveraging the latest in AI and ML, businesses can automate time-consuming processes that would otherwise be difficult to handle. This frees up staff to focus on delivering a better customer experience. This in turn increases customer retention and ultimately leads to revenue growth.
Hybrid recommendation is a method of using multiple types of recommendation systems in order to overcome the limitations of each. This approach is a popular solution for solving real-world recommendation problems, as it can improve recommendation quality significantly.
For example, Netflix uses a hybrid model to spot similar users through collaborative filtering and identify movies that have similar characteristics through content-based filtering. The result is a more accurate and diverse set of recommendations that benefit both the user and the company.
There are several ways to implement a hybrid recommendation system, including weighted fusion, cascading, and meta-level. The weighted fusion technique involves combining the scores from different recommender systems in a specific way. The cascade hybrid model provides a hierarchy of recommendation engines, with the lower priority ones breaking ties in scoring of the higher-ranked ones.
Another way to incorporate a hybrid recommendation algorithm is to use explicit and implicit feedback data to enhance the performance of a machine learning recommendation system. Explicit feedback includes ratings and comments that users provide on products or services, while implicit feedback data is information about preferences that the user doesn’t explicitly express.