Brand
Home Awards Services Case Studies Certificates About Blog GET FREE QUOTE

PRODUCT RECOMMENDATION SYSTEM

12.jpg

E-commerce Product Recommendation System

A CPG Client chose Ezapp Solution to build a highly efficient product recommendation system. 

The Recommender system has the ability to predict the right kind of product for the particular user. Based on the user’s profile, customer behavioural data, the system could detect whether a particular user would prefer an item or not. 

The personalised recommendations given to users, assists the user to make correct and precise decisions for an online transaction, Getting an appropriate recommendation makes the user spend less time doing online transactions which increases the sales. 

So simply convert your shoppers to clients and gain their loyalty to your business is directly proportional to increment of sales .

Introduction to Recommendation System 

Due to the prevalence of the internet, a user has too many options to choose from and a recommender system is felicitous in such situations . 

Recommendation system is a system or algorithm which is capable of predicting the close preferences of items or products for any user in the future and recommends the top closest preferences.

It filters the most relevant information for a given user out of all the necessary available information. Various online platforms use this engine for recommendation like Amazon for product recommendation, Netflix for movie recommendation. Other personalised products such as Alexa also use this type of algorithm. 

 

COLLOBORATIVE FILTERING  

CONTENT BASED FILTERING 

  • Looks for similarity between user and items.

  • Looks for similarity between items.

  • Recommendations are based on the items other similar users liked.

  • The recommended items are based on the previous items or feedbacks.

  • If a user P is similar to user Q, and user Q likes hair dryer, then the system can recommend same item to user P (even though user never bought such an item).

  • If a user likes chocolates, the system would recommend chocolate ice cream or chocolate based products.

       

Another popular technique for building a recommendation system which comes with proofing to the challenges faced by content or collaborative based filtering. 

 i.e, Deep Neural Networks 

It depends on business to business, for choosing the  best developed approach for building the recommendation system. There are other different types of recommendation system. 

There is a Risk-aware recommender system, in this particular system, your web browser kind of gives you alert for high risk websites or this website might not be safe to use. 

Hybrid Recommender system is  a class of recommender system where it can predict and suggest any restaurants or places based on the information provided or on user preferences.

Implementation of Collaborative Filtering Recommendation System 

CPG based corporations or businesses chose Ezapp Solution to build a highly efficient product recommendation system, the recommender system is a proven beneficial system for both service providers and customers or users. The system has the ability to predict the right kind of product for the particular user.

Based on the user’s profile or say behavioural data, the system could detect whether a particular user would prefer an item or not.

Given below are a few rows of our sample data, the dataset is picked from an e-commerce store. 

1. We are importing this dataset to build our proficient recommendation system . 

2. A pivot table between user and items purchased by the user is made, the similarity between different related users is calculated via the cosine similarity method            which measure the or similarity between two vectors.   

3. Selecting one user and its similar user and looking for the items which the former user bought but latter did not buy.

 4.Recommending the top 2 or 3 items for the user, directly related to the first similar user.

Recommendation: