Understanding Recommendation Engines in AI

Humans For AI
Jun 3, 2017 · 6 min read

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

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.

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Figure 1: Understanding Recommendations Engine

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?

Definition of Recommendation Engine

“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

  • 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

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?

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Figure 2: Types of Recommendation Engines
  • 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

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!

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