Five common machine learning recommendation algorithms

Personalization is the current trend. Whether it is e-commerce, virtual assistants or short videos, recommendation algorithms use artificial intelligence (AI) to display personalized products and services to users.

The current recommendation algorithm is mainly based on collecting user data from social media, websites, e-commerce portals, applications and other channels, and using this data to train artificial intelligence (AI) with machine learning capabilities.

Next, we bring 5 common machine learning recommendation algorithms:

1. Collaborative filtering

Collaborative filtering (CF) is an ancient and classic recommendation technology , used to match users with similar interests to personalized items, people, or feeds. In short, collaborative filtering can help users discover other similar products through "customers who bought this item also bought it" type recommendations. It works by inferring users’ preferences for a certain type of product based on their purchasing behavior and recommending more similar products to them. By analyzing users who purchased similar products today, we can predict the products they are more likely to purchase in the future. This method is widely used in personalized recommendation systems to provide users with a better shopping experience.

2. Content-based filtering

The second recommendation algorithm is content-based filtering, which uses purchased products as input data to recommend similar products. This recommendation method works for each specific user and can be applied to a large user base.

Such recommendations can usually be found in products such as e-commerce portals, short videos, and digital libraries.

3. Personalized Video Ranking

The Personalized Video Ranker (PVR) algorithm originated from the urgent need for OTT (Over-the-top) services to determine user preferences.

Taking Netflix as an example, the boom in digital content production and demand prompted it to develop a unique and effective recommendation system.

Supervised and unsupervised machine learning algorithms enable Netflix to strike the right balance between personalized and non-personalized content recommendations.

The PVR algorithm obtains the best matches from the entire database catalog in a personalized way for each user, combining personalized filtering with the highest-ranked web series.

4. Deep Neural Network

Use deep neural network to analyze each user’s historical records, including likes, comments and most commonly consumed digital content. Predict future user preferences with precision and relevance. Coupled with the ranking algorithm, richer features are extracted for each content to rank recommendations.

5. Knowledge-based recommendation system

Knowledge-based recommendation system is supported by rich, diverse and rapidly changing data sets. Capture knowledge stored digitally in the backend by decoding data intent, context, to match specific user queries.

This kind of recommendation system with machine learning capabilities can greatly increase its knowledge in vertical fields. The unique thing about this knowledge-based recommendation algorithm is that it can be continuously improved.

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