Understanding Recommendation Engines in AI
Written by Deepa Naik
If you decide to conduct a study on consumer behavior in shopping and take a survey of “people who ‘do not’ enjoy shopping”, there will only a meagre percentage of them in the category ; however you take a headcount of “people who do not like to shop alone” and yes, your poll just changes drastically. Anyone who wants to shop, never ever wants to do it alone. This behavior of having “company” for shopping may on the outside just seem to be a characteristic of man as a social animal, but there is more to it than just that.
Traditional Shopping vs. Shopping Experiences Today
Growing up we have always looked for the company for shopping. Just take shopping for clothes, for example, we have always asked for advice — be it your siblings as kids or your besties at college or colleagues at work. Shopping trips traditionally were hours or even day long trips — researching the latest fashion, driving the bargains across various shops and the try-out sessions. However as time progressed, shopping trips started becoming a short affair and the besties and friends were replaced by the more “professional” personal shopper — who in turn could give you good recommendations for “the look” and “the image — the corporate meeting look, the cocktail party et al. Times were changing… are changing but one thing was sure, you still wanted the recommendations.
Currently, shopping trips have become even shorter and it just takes a few minutes and a few clicks on the internet. The recommendation and advice are coming as messages in emails or advertisements — exclusively tailored and personalized for you. The handbag that I shopped for the other day was less than a five-minute shopping trip — online. Generally, it takes me hours to choose a bag. This time I received some rather tempting recommendations of bags in my email and all I had to do was click and pay and wait for the delivery to happen. The catch here was that I had bought my earlier bags online and they knew exactly what I liked and didn’t.
Understanding Recommendations Engine
Recommendations Engines — one of the concepts in Artificial Intelligence is fast gaining momentum. It is a perfect marketer tool especially for e-commerce / online businesses and is very useful to increase turn around (sales, profits etc.)
What is a Recommendation Engine?
Recommendation Engines (also called as Recommender Systems) started off becoming popular in the retail industry, mainly in online retail/e-commerce for personalized product recommendations. One most common usage is for Amazon’s section on “Customer who bought this item also bought …”. Recommendation Engine is seen as an intelligent and sophisticated salesman who know the customer taste, style and thus can make more intelligent decisions about what recommendations would benefit the customer most thus increasing the possibility of a conversion. Though it started off in e-commerce, it is now gaining popularity in other sectors, especially in Media. Some of the examples are YouTube “Recommended Videos” or Netflix “Other Movies You May Enjoy”. Other industries are beginning to use recommendation engines, such as the transportation industry. Waze uses it for intelligent navigation systems; IBM uses it for traffic control systems. Lately, GE started a Kaggle competition to find the best routes to save energy for the airline industry.
Definition of Recommendation Engine
In their paper titled “Recommendation Systems: Principles, methods, and evaluation”, F.O. Isinkaye et al define Recommendation Engines / Recommender Systems as follows:
“Recommendation Engines / Recommender systems are information filtering systems that deal with the problem of information overload by filtering vital information fragment out of a large amount of dynamically generated information according to user’s preferences, interest, or observed behavior about the item. Recommendation Engines / Recommender system has the ability to predict whether a particular user would prefer an item or not based on the user’s profile”
Recommendation Engine — Examples
Some examples of recommendation engine usage are seen in the following
- Facebook — “People You May Know”
- Netflix — “Other Movies You May Enjoy”
- LinkedIn — “Jobs You May Be Interested In”
- Amazon — “Customer who bought this item also bought …”
- Google — “Visually Similar Images”
- YouTube — “Recommended Videos”
- Waze — “Best Route”
Recommendation Engines as Filtering Systems
As we move into an era of data explosion, it is becoming more and more relevant to find ways to scan through the huge amount of data. Recommendation Engines become a great tool for filtering and ensure that the consumer gets to see the data that is relevant for his taste, his style and preferences and ensures he spends minimum time searching for the right data.
E-commerce / online stores carry a large product listing. If you want to buy an item on Amazon, you will find the listing in thousands, not just a few hundreds. Out of this vast sea of products we want to ensure that we present the most appropriate and the most relevant recommendation to the customer.
For a recommendation system to be good another important characteristic is it should be able to continuously learn and adapt itself flexibly to new user behavior. It also needs to be providing data real time. For example, a large number of special offers, changes in the assortments and price changes that happen make good recommendations obsolete shortly after having been made. A good recommendation engine must, therefore, be able to act in a very dynamic environment.
How do Recommendation Engines Work?
Recommendation systems are based on algorithms that “learn” from past data. The data used maybe about the products preferred liked or bought by the customer in the past or it could be products preferred, liked or bought by “similar” customers. Based on this criterion the following types of recommendation engines are built.
- Collaborative Filtering
This is based on customer’s behaviors, activities or preferences and predicting what customers will like based on their similarity to others
- Content-Based Filtering
This is based on items liked by the customer and keywords used to describe the items. It also takes into consideration the preferences chosen by the customer
- Hybrid Recommendation Systems
These are becoming popular where the combination of both the methods listed above is used. There is a trade-off that needs to be made in what to filter.
Developing models for product recommendation algorithms is a growing research area. This deals with a field of Artificial Intelligence called machine learning and related techniques.
Conclusion
Recommendation Engine is your companion and advisor to help you make the right choices by providing you tailored options and creating a personalized experience for you.
Currently, it is seen in online retail and media industries. It is catching up in transportation. Other industries where it is gaining fast acceptance is financial services (example: granular view of a customer can help augment existing fraud detection techniques), healthcare ( example: personalized health care by analyzing vast amounts of information regarding an individual such as patient history, electronic medical records, lifestyle information, etc. ).
It is beyond a doubt that recommendation engines are getting popular and critical in the new age of things. It is going to be in the best interest to learn to use recommendation engines for businesses to be more competitive and consumers to be more efficient.
In a nutshell, recommendation engines are a contemporary form of artificial intelligence at play.
Reference:
http://dataconomy.com/2015/03/an-introduction-to-recommendation-engines/
https://www.toptal.com/algorithms/predicting-likes-inside-a-simple-recommendation-engine
http://www.sciencedirect.com/science/article/pii/S1110866515000341
https://mapr.com/blog/recommendation-engines-driving-customer-interactions-next-best-action/
About the Author:
Deepa is a founding member of Humans For AI, a non-profit focused on building a more diverse workforce for the future leveraging AI technologies. Learn more about us and join us as we embark on this journey to make a difference!
