Recommendation System

An increasing number of online companies are utilizing recommendation systems to increase user interaction and enrich sale potential. Use cases of recommendation systems have been expanding rapidly across many aspects of eCommerce and online media over the last 4-5 years, and we expect this trend to continue.

Our Challenges while using Recommendation Systems

Cold Start Problem

Cold start is a potential problem in computer-based information systems which involves a degree of automated data modelling

Changing User Preferences

The issue here is that while today user have a particular intention when browsing e.g. ,Amazon – tomorrow users might have a different intention.

Lack of Data

Perhaps the biggest issue facing recommender systems is that they need a lot of data to effectively make recommendations.

Unpredictable Items

The $1 Million prize offered by Netflix for a third party to deliver a collaborative filtering algorithm that will improve Netflix’s own recommendations algorithm by 10%

Benefits of using a recommendation system

More upsell activities

The bottom line of utilizing recommendation engines is to create an experience that makes the visitor feel understood and served well as individuals.

Enriched Customer profile

As users are engaged with recommended content more granular customer profiles and personas are automatically built, which helps with targeting look alike audiences.

User Engagement

Recommendations can increase content consumption, shorten the path to relevant content and boost time engaged with your brand.

Tech Stack

The architecture of recommendation system involves a database, caching database (for real time recommendations), messaging pipeline for capturing user interactions, platform like Jupyter Notebook/Databricks to run python module and a VM to deploy the module