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Quick Guide to Recommendation Engines

Data & Analytics

According to a study, 76% of consumers get frustrated by businesses who do not offer personalized experiences. Meanwhile, 71% expect personalization. This highlights the shift in consumers’ purchasing habits and the urgent need for companies to adapt. A way to personalize customers’ experience is through recommendation engines. 


What are recommendation engines?


Recommendation engines utilize predictive analytics to assist companies in anticipating their customers’ desires and requirements. By analyzing a business’s unique historical and behavioral data, these engines employ machine learning and statistical modeling to produce advanced algorithms. These algorithms rely on a combination of factors. One of them is customer’s past behavior and history. Another is ranking of products by consumers. Lastly, behaviors and history of similar groups.


What are different techniques of recommendation engines?


Collaborative Filtering

The collaborative filtering technique gathers and evaluates data on user behavior, online activities, and preferences. The goal is to anticipate user preferences based on their similarity to other users. It employs a matrix-style formula to graph and compute these similarities. The advantage of using it is that it does not require an understanding or analysis of the object (e.g., products, films, books) to accurately recommend complicated items. It selects suggestions based on what it knows about the user. It does not rely on analyzable machine content. For example, if user X enjoys reading books A, B, and C while user Y prefers books A, B, and D, they have comparable interests. Therefore, it is likely that user X would choose book D and user Y would enjoy reading book C. This is how collaborative filtering operates.


Content-Based Filtering

Content-based filtering is based on the idea of describing a product and creating a user profile of preferred choices. It assumes that if a user likes a particular item, they will also like other items with similar keywords (such as genre, product type, color, and length). An algorithm is used to evaluate the similarity of items based on cosine and Euclidean distances. An advantage of using it is that it does not require additional data about other users. This is because the recommendations are personalized to the individual user. Additionally, it can identify and recommend niche objects that appeal to specific interests. For instance, if a user X enjoys action movies like Spider-man, this technique would recommend other action movies or movies featuring Tom Holland.


Hybrid Model

Hybrid recommendation systems use both collaborative and content-based data to provide users with a wider range of recommended items. This technique involves assigning natural language processing tags to each item and using vector equations to calculate similarity. The collaborative filtering matrix then suggests items to users based on their behavior and intentions. The advantage of this is that it is considered to be more accurate than the previous methods mentioned. As an example, Netflix uses a hybrid recommendation engine that analyzes user interests (collaborative) and recommends shows/movies that share similar attributes with highly rated content (content-based).


How are recommendation engines developed?


The foundation of a recommendation engine is data, which is analyzed by algorithms to identify patterns. The quality and quantity of data play a crucial role in the engine’s ability to provide accurate and effective recommendations that can boost revenue. Typically, a recommendation engine works by utilizing both data and machine learning algorithms in four phases. Let’s take a closer look at these phases.


Step 1. Data Collection

To construct a recommendation engine, the initial and essential phase is to collect the relevant data for each user.There are two types of data: explicit and implicit. Explicit data contains information collected from user inputs such as ratings, reviews, likes, dislikes, or comments on products, meanwhile implicit data contains information gathered from user activities such as web search history, clicks, cart actions, search log, and order history. Over time, each user’s data profile will become more distinctive. However, one must not forget to collect customer attribute data such as demographics (age, gender), psychographics (interests, values) to identify similar customers, and feature data (genre, object type) to determine similar products likeness.


Step 2: Data Storage

After collecting the data, the next step is to ensure efficient storage of the data. With the increasing amount of data collected, it is important to have scalable storage options available. There are various storage options available, such as NoSQL, standard SQL databases, MongoDB, and AWS, depending on the type of data being collected.When deciding on the ideal storage option, certain factors should be taken into account, such as the ease of implementation, storage capacity, integration capabilities, and portability.


Step 3: Analyze The Data

Once the data is collected, it needs to be analyzed to provide accurate recommendations. There are various methods to analyze data, including real time analysis, batch analysis, and near-real-time analysis. Real-time analysis is a method used in recommendation engines where events are evaluated and analyzed as they happen to provide instant recommendations. On the other side, batch analysis involves periodic processing and analyzing of data, and is often used when sending recommendation emails. Lastly,  near-real-time analysis falls between the two, where data is analyzed and processed within minutes rather than seconds, and is often used to provide recommendations while the user is still on the website.

To take it one step further, it is worthy to note that some of the most popular and widely used libraries for recommendation engines are python, Java and R. When we mention python, we mean specifically Scikit-learn, Surprise, TensorFlow Recommenders, LightFM, PyTorch. Meanwhile, Java includes Mahout, Lenskit, and Apache Spark. As for R, it includes RecommenderLab, Tidyr, and Recosystem.

Regarding what is trending, it is important to mention deep learning. Deep learning-based recommendation systems using neural networks are becoming increasingly popular. TensorFlow Recommenders and PyTorch are two popular libraries for building such systems. Additionally, there is growing interest in using graph-based algorithms and techniques for building recommendation systems, and libraries like GraphSAGE and Deep Graph Library (DGL) are gaining popularity.


Step 4: Filtering The Data

After analyzing the data, the last step is to filter the data accurately to deliver useful recommendations. Various matrices, mathematical principles, and formulas are applied to the data to provide appropriate suggestions. It is essential to select the appropriate algorithm, and the result of this filtering process is the recommendations.


To conclude, with the rapidly growing data over the internet, it comes to no surprise that Netflix can predict which movie you’ll want to watch next, or which product you might like on Amazon. For this same reason, it has become increasingly important for businesses to use AI to search and map relevant data for users. By doing so, they can enhance the consumer experience and drive the trend of digitalization, while also keeping up with the rising competition among multiple enterprises.

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Featured Image by shayne_ch13.com on Freepik


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