The growth of the internet and the 4th industrial revolution of information technology has led to the growth of various services are offered on the internet companies such as Amazon E bay Walmart have been able to reach out to customers in providing e-commerce platforms for buying goods and service this stretches to the entertainment field whereby streaming of music, movies videos is widespread with the likes of Spotify, youtube and Netflix
Have you ever wondered how Netflix makes recommendations based on what you’ve already seen as you use these platforms? Alternatively, how do e-commerce websites display choices like “Frequently Bought Together”? While they appear to be easy choices, a sophisticated statistical algorithm is used behind the scenes to predict these recommendations. That’s where recommender systems come in
Examples of platforms that use recommendation systems
So What are recommender systems?
Recommender systems (RS) are algorithms that leverage historical data to recommend or suggest a particular product, service, person, or entity by inferring correlation strength. Recommender systems use a collection of techniques and algorithms that can suggest “appropriate” products to users.
It begins by capturing a customer’s historical data and then suggests items that the users are likely to purchase based on that information. The suggested products should ideally be as crucial to the consumer as possible to be engaged with: YouTube videos, news stories, online goods.
Applications of recommender systems
Recommender systems showcase items that may be relevant to the User, but how is the application of recommender systems be beneficial to the User relevant to the organization and developers
here is why. All industry domains use Recommender Systems to strengthen their customer-centric approach and better serve their customers by:
- Up-selling, cross-selling, and Business Basket Analysis strategies for hypermarkets and e-commerce websites (Walmart, Carrefour, Amazon, eBay) increase retail revenues.
- Increasing use satisfaction: The shortest path to a sale is beneficial to both you and your customer because it reduces their effort. Recommendation systems allow you to shorten the path to a sale for your customers by suggesting a suitable alternative to them, often even before they search for it.
- Increasing sales and conversions: There are very few ways to boost revenue without putting more money into marketing. You get recurring additional sales without any effort once you set up an automated recommendation system.
- Increasing customer loyalty: It increases consumer familiarity with your brand and user experience by having them spend more time on your website, raising their likelihood of making potential purchases from you.
Types of recommender systems
Recommender systems increase sales, loyalty, and saving time, but how do we implement recommender systems? Which type of recommender systems should we use in different scenarios?
Let’s have a look at types of recommender systems
Types of recommender systems
The diagram above is a summary of types of recommender systems based on memory/similarity or model. The most common is collaborative filtering and content-based; hence focuses more on this article on those two for now
Let’s have a brief look at model-based types of recommender systems, although collaborative filtering also uses models such as Matrix factorization, clustering
Model-Based recommender systems
Model-based approaches use a training stage to learn parameters or patterns using an optimization algorithm such as gradient descent rather than using the entire Dataset each time. Instead of computing the entire matrix to estimate user preferences, we use machine learning algorithms such as neural networks as function approximators to perform the approximate nearest neighbor search. Consequently, it provides benefits of both speed and scalability. Model base use probability and machine learning to carry out recommendations
Memory/similarity based models
Memory-based RS uses neighborhood techniques to determine how similar users and objects are (similarity measures). Euclidian distance, Cosine distance, and Pearson correlation coefficient are the most common.
Conclusion
recommender systems are self-explanatory algorithms that leverage historical data. Recommender systems use a collection of techniques and algorithms that can suggest “appropriate” products to users. They capture a customer’s past history and then suggest items that the users are likely to purchase.
Model-based approaches use a training stage to learn parameters or patterns using an optimization algorithm. It provides benefits of both speed and scale
Memory-based RS uses neighborhood techniques to determine how similar users and objects are (similarity measures).
In the next article, there are three types of memory-based/similarity-based models discussed: collaborative filtering, content-based and hybrid methods
NEXT UP: TYPES OF MEMORY/SIMILARITY BASED MODELS
References
- https://medium.com/@fenjiro/recommender-systems-d0e597424a98
- https://www.cs.carleton.edu/cs_comps/0607/recommend/recommender/itembased.html
- https://predictivehacks.com/item-based-collaborative-filtering-in-python/
- https://towardsdatascience.com/alternating-least-square-for-implicit-dataset-with-code-8e7999277f4b
- https://www.offerzen.com/blog/how-to-build-a-content-based-recommender-system-for-your-product
- https://pub.towardsai.net/item-based-collaborative-filtering-in-python-58f21d959c1
- https://www.geeksforgeeks.org/user-based-collaborative-filtering/
- https://www.kaggle.com/dasmehdixtr/user-based-collaborative-filter-example
- https://buomsoo-kim.github.io/recommender%20systems/2020/09/14/Recommender-systems-collab-filtering-10.md/
- https://www.kaggle.com/amiralisa/context-aware-recommender
- https://github.com/yadavgaurav251/Context-Aware-Recommender
