Ministry of digital technologies of the republic of uzbekistan

2.2. How Do Recommender Systems Work?
A recommendation system is a data filtering engine that uses deep learning concepts and algorithms to suggest potential products depending on previous preferences or secondary filtering.
The concept behind such algorithms is finding patterns in a consumer or similar consumer behavior towards a service or product.
The method by which data is collected varies greatly depending on the type of products or services sold. For example, data collected on e-commerce websites would be in review ratings, while Youtube would save liked and disliked videos.
RECOMMENDATION SYSTEMS ALGORITHMS
3.1.Benefits of using Recommendation systems
1.Increased Sales: The number one reason why companies invest in such systems is to generate revenue. Increasing the sales through recommendation systems also increases consumer engagement on their site and captures longer session times.
2.Lower System Load: As the system does filter the most matching items for each given user, recommendation systems improve sales while maintaining a lower load on the system and decreasing costs in the long run.
3.Increasing Engagement and Satisfaction: By continuously providing consumers with an endless array of personalized products, consumers will continue to engage with the application/website. Recommendation Systems optimize the experience to reduce wasted page real estate to boost satisfaction with related content.
3.2. Types of Recommendation systems
Depending on the products or services that a business offers, different recommendation systems may be put into place. Some examples of different systems are:
Collaborative Filtering
The collaborative filtering method focuses on the similarity between different users and items. Consumers who share an overlap of similar interests will more than likely be interested in other similar products. These similarities can improve recommendations to all users within the data set and continue to learn as new products come into the market.
For example, if Alex likes football and buys a pair of cleats and Meg likes football, thenMeg will more than likely also be interested in those cleats.
There are several types of collaborative filtering:

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  User to Product Filtering is the simplest of all the filtering methods, in which the algorithm will look for similar items that a consumer previously purchased or liked. Genre, price, item category, etc. are all categories that influence filtering.
  
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  User to User Filtering works by finding consumers who share similar interests and suggests products and services based on what his look-alike user has chosen. Such an algorithm requires high computational power and resources as the algorithm will need to compare all the users in real-time.
  

Content-Based Filtering
Content-Based Filtering recommendation algorithm evaluates the similarity of products. The recommendation system will suggest products with similar classifications to the user previously interacted with.
For example, if the last three watched movies included the comedy genre, the system will recommend other similar comedy movies or shows. Such recommendations are also imperative with product images using Image Processing or Natural Language Processing to match items that look, are titled, or described similarly.
Note that similarity-based recommendations will suffer from the cold start problem. The cold start problem occurs when there is not enough preference data. The recommendation system can not accurately suggest great options when initially implemented on the platform since it takes time to gather and train.
Hybrid Filtering
Hybrid filtering utilizes both collaborative and content-based filtering, utilizing the advantages of each other.
Several studies comparing the performance of hybrid filtering systems with the collaborative and content systems alone have shown that hybrid systems have better accuracy.
Combining both algorithms can remove multiple issues like the cold start problem and help gather data quickly. Many of our favorite sites like Google, Youtube, and Netflix utilize a hybrid filtering in their recommendation systems.