Empower Your Business With The Magic Of Machine Learning!
- +30 210 9701777
- Aristotelous 83, Athens 163 43
- contact@mobiplus.co
Empower Your Business With The Magic Of Machine Learning!
Recommendation engines have now become the central feature of any business.
Companies such as Amazon , Netflix , Goggle , TikTok , Ebay , Youtube , JD.com , Alibaba , Helly Hansen , Spotify , Facebook , Pinterest , Linkedin , Match.com, Tencent, eHarmony , Quora , Github , Gmail ,Tinder , Booking.com , Bing , Stitch Fix , Toutiao , Bytedance , Baidu use Recommendation Engines.
Every successful and innovative Digital and non-Digital business uses Recommandation Engineering.
The new era of the digital revolution is called Recommendation Engineering.
31% of International e-commerce comes from Product Recommendations.
Every business guesses (Predict) what the customer wants and recommend it!
Tim O-Reilly entrepreneur , Digital Publisher and thought leader says :
A truly new application should get better the more people use it.
It should learn their needs and help them reach their destination without friction.
Every time a user clicks on a product, sees a product, buys a product, shares a product, likes a product your business should get better.
It should use all users and learn from them.
To become better using everyone’s knowledge.
This is Recommendation Engineering.
It learns using Machine Learning what your customers want and recommends to each of them individually what they want.
Greg Linden who helped develop Amazon Recommender says:
WEB 2.0 applications should automatically learn, adapt and improve based on user needs.
They should automatically get better every day.
Jeff Bezos in an interview with the Harvand Business Review said:
We don’t make money when we sell things.
We make money when we help our customers decide easily and correctly what they want to buy.
We give them Options.
The right choice for each individual!
This is Recommendation Engineering!
Let’s give an example of how you look like as business without Recommendation technology in e–shop and in your physical store.
Imagine you go home at night around 21:00 after the office.
You sit in front of the TV with a glass of wine to enjoy a Netflix movie.
Netflix has about 4000 movies today.
Let’s assume that the movies are presented in the same way as you present the products in your e-shop.
List with photos and descriptions , 4 movies in each row and several columns down.
It has them by category Adventure , Science Fiction , Comedy , Children’s and has a search too as well.
You’re looking for a movie to watch.
You scroll down , you click , see plays description to find the appropriate play.
I dont think you are to see a movie doing all that.
Okay … some people would stay on the set for 20 minutes and find amovie .
But instead of 300 million customers Netflix , would have 5 million.
That’s what you lose when you don’t have a Recommendation Engine in your e-shop and in store !
Recommendation engine is therefore the Key feature of any business in the web.
To learn how the top companies in the world increase revenue up to 30% from existing customers using Recommendation Systems and Personalization read the book below.
https://mobiplus.co/ebooks/customer-rediction/
Recommenders consists of algorithms which convert shopping data , views, likes , products and other into shopping recommendations .
They achieve this by finding, calculating and prioritizing the most interesting correlations and co-occurrences within the data of users and products of the company.
They are constantly improving themselves using the above daily data of the company.
They predict to which category a piece of your business data belongs.
Is it male/female , Luxury product , Luxury product , family , cheap , expensive , color , what is the customer sentiment?
They predict through the data of the company a continuous result e.g. propensity to buy.
That is classification can predict if it will rain tomorrow and regression how much water will be poured.
In the Recommendation Engine space of a clothing e-shop the Classifier predicts which clothes you are likely to buy and Regression predicts the order you will buy them.
Thus
Predictive personalization is the basic recipe for recommendations.
Messages such as “People who bought this bought that” come from the Recommendation engine above.
But what are the common features that your products have in common?
What are those characteristics, from the customer’s point of view, which imply common tastes and desires between customers?
E.g. a kind of similarity might be a quirky but charismatic actor in a play who makes the play unforgettable , a musician whose sounds create a particularly eccentric sound!
Quantifying eccentric similarities make the recommendations more Personalized and Challenging in a pleasant sense.
Surprise for something new and exciting, something you didn’t expect, something that will take you further and closer to where they want to go !
The paradox of the genius of Similarity is the promise of the Predictable Surprise known as serendipity.
In this way the Recommenders plan the music you will listen to and give you energy for your day, recommend restaurants to try new flavors, find friends you didn’t know until today, find your next relationship!
Recommendation engines aim…
There are three dominant architectures for Recommender Systems
This architecture is dominated by product features.
This is where the similarities between users dominate.
Imagine here a huge excel sheet with Users in the rows and Products in the columns.
Each cell indicates the degree of the user’s desire for that product.
In some cell we will have elements for the user’s desire, click, view, buy, like and in some cell we have nothing.
In the majority we have nothing.
Like a Swiss cheese full of holes.
In each cell we have to calculate the desire of each user for each product.
This is a technical and mathematical problem.
That’s the job of the Recommender.
But how are recommendations generated from the data?
They are created by Finding, Defining and Concluding desired similarities.
Each product in the previous table has characteristics and features
Each user also in the previous table has attributes and traits.
We therefore compute the correlation between product and user attributes traits.
Greater correlation implies greater similarity.
So this is where Recommendations are created.
They combine both of the above to create recommendations that are superior to each of the above individual architectures.
The simplest success stories are the most popular ones which can be automatically suggested by the recommender and are the first way to overcome the cold start problem when you have no data about the customer.
These techniques are used to find when a product is bought what other products exist that are bought together.
They use older retail shopping data so that associations and which products are bought together can be found.
Associations are calculated at the customer level, e.g. what he has in his account, and Market basket analysis is calculated at the market level (in the basket or checkout)
Algorithm used to compare Blogs and stories, texts.
Here the algorithm defines Vectors (group of characteristic words in the text) and then compares these texts through their vectors.
Cosine Similarity calculates the angle between two vectors and thus how similar they are.
It could be used to read the description of the company’s products and find out how close one product is to another.
Imagine your data as Galaxies in the sky.
The closer they are the more similarities between them!
A Convolution Neural Net architecture specialized for image recognition applications in apparel footwear home equipment and in particular where photography plays a role in the market.
This system can identify features within the product photo and from there the other algorithms can extract similarities.
These way you can predict a customers style when they enter the store and recommend products with the same style.
Mixed effect models are used in places where data are scarce.
An example is the apparel sector where products are coming out in a small amount and for a limited time, e.g. summer 2021.
Naïve Bayes algorithms are used in cases where we don’t have enough data e.g. consumer opinion of the product in clothing stores.
Machine Learning=Representation+Evaluation+Optimazation
Representation
It involves converting the business data into a space that can be translated for better understanding by algorithms.
Evaluation.
Calculating the accuracy of the recommender and how deeply what will be learned, how the recommender will evolve as Pedro Domingo’s. says.
Will he learn to predict Purchases that customers will make ?
Will he l learn to recommend the most profitable product for the business ?
Will he learn to recommend the product that makes the customer most happy?
An important decision for every business!
Optimization.
This is the last part of the system and this is where we improve the Functionality of the system.
Here are used :
Multiarmed bandit algorithms that reward the consumer’s decision to buy the product
Epsilon greedy algorithms that help customers find striking new products to buy.
I think this new world is impressive.
It creates an amazing Mechanism in your business , which continuously learns how to make your customers happy.
And in the next step it recommends what new products you should produce to keep your customers happy.
Lartigiano increases revenue and cart using Artificial Intelligence!
See how he did it here!
Contact us now , to see how the mobiplus shopping recommendation platform can give you the above features and increase your revenue. 30%.
mobiplus member Elevate Greece
Confirmed Innovation.
Test out the Recommendation Engine for up to 20.000 euros in revenue from recommendations. No credit card required. Just enter your company information and we’ll contact you with all the details.
