Recommendation systems, or recommender systems, are software engines designed to suggest items to users depending on previous likes and dislikes, product engagement and interaction, etc. Recommender systems keep users interested in whatever the site continues to recommend.
Recommendation engines provide a personalized user experience, by helping every single consumer identify and discover their favorite movies, TV shows, digital products, books, articles, services, and more. These systems help businesses increase sales and benefit consumers. Amazon lists millions of products on its website; users will likely face issues navigating and finding which products to buy. With Recommendation Systems, consumers can easily find products, promote ease of use, and compel consumers to continue using the site versus navigating away.
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 System Life Cycle
Some recommendation systems are far more complex than others, but many follow a seven-step path to develop a successful recommendation model:
- Collect the Data: Identify and collect data relevant to the recommendation system. For example, Amazon can collect reviews and product ratings (5-star rating system), while Netflix stores watched, like, and bookmarked shows and movies.
- Store the Collected Data: Store data in proprietary data warehouses. Or utilize third-party cloud service providers such as Amazon, Google, MongoDB, etc. for greater efficiency and data retrieval speed.
- Filter the Data: Filter problematic values (null, infinite, or misleading) in the dataset to improve model accuracy.
- Analyze the Data: In the case of recommendation systems, analyzing data means feeding it into machine-learning or deep learning algorithms that can detect hidden insights and patterns.
- Evaluating and Test Our Model: Check the performance of the recommendation system model. If the model performs poorly, tune the hyperparameters to the desired performance.
- Deploy Our Model: Your model is ready to be deployed into actual practice. Continue to monitor and tune the system.
- Online Machine Learning: Online machine learning after deployment can continuously improve and adjust the model by learning from newly acquired data, maintaining longevity.
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Recommendation System Algorithms
Matrices and deep learning are ways to build a recommendation system. Where matrices are logic-based systems with
- Clustering: an unsupervised machine learning algorithm that can return good prediction results. In most cases, Clustering alone would not be sufficient to build an advanced recommendation engine.
- Deep Learning: a more complex analytical approach that takes as input the consumers' behavioral patterns and filters down the most relevant suggestions to each user.
Benefits of Using Recommendation Systems
- 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.
- 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.
- 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.
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:
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:
- 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.
- 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 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 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.
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Real-Life Recommender System Examples
With millions of products on Amazon, consumers may get distracted by what they want to buy; an increase in product variety will result in increased consumer decision-making time.
Amazon recommendation systems filter in likely items to help consumers find a satisfactory product.
Spotify evaluates which songs its users enjoy listening to and will recommend new music accordingly. They also curate a Discover Weekly playlist for users to discover new yet familiar music.
Spotify's hybrid filtering algorithm helps listeners discover new music by learning their likes, dislikes, and nuances.
Facebook / Meta
Facebook also utilizes multiple recommendation systems throughout its app. These engines recommend the next post, friend suggestions, and ad placements based on likes, dislikes, mutual friends, and more.
As with the previously stated companies, Facebook's revenue is directly correlated to the effectiveness of its recommendation system.
Netflix is known for its extensive use of recommendation systems. With over 80% of content watched on Netflix coming from algorithmic suggestions, their recommendation system generates an estimated 1 billion dollars of revenue per year.
Furthermore, new Netflix accounts will rate popular shows and movies to help the recommendation algorithm predict new shows to avoid the cold start problem.
Google and Youtube
With one of the most popular search engines and browsers available, Google spends fortunes updating its recommender system efficiency and accuracy as much as possible.
In the search engine, Google will generate auto-fill results based on recent searches to help users find what they are looking for, increasing user satisfaction.
Google also deploys recommendation systems on Youtube by implementing personal suggestion and rating systems using filters like views, likes, shared videos, subscriptions, genres, and more. Youtube also utilizes popularity suggestions to generate views on highly trending videos.
Google advertising generates a large share of the revenue. Google stores behavioral data such as purchases, Youtube videos viewed, and searches made to provide and suggest advertisements that match products and services to the user.
Final Thoughts on Recommendation Systems
Collaborative Filtering, Content-Based Filtering, and Hybrid Model recommendation engines are high-level foundational methods for anyone to get started with this great tool. With a general overview of the system, there are factors to keep in mind when building a Recommendation System include:
- How to track the effectiveness of the recommendations
- When to stop recommending a product to a consumer after they have stopped interacting with it
- How to weigh products that have higher reviews or view counts
- Do recommendations change dynamically? How far back will the system look?
- Recommendation systems can inadvertently pigeonhole a consumer into a small category. How can your algorithm recommend different but effective content to the consumers?
Many businesses utilize and improve upon recommendation systems every day. These algorithms will continue to be developed and used for more and more applications. Such systems offer benefits to both consumers and organizations alike; when used effectively, they can prove to be an imperative sales tool for any company wanting to provide their consumers with satisfying products and recommendations.