JanataHack: Recommendation Systems

In today’s world, every customer is faced with multiple choices. For example, If I’m looking for a book to read without any specific idea of what I want, there’s a wide range of possibilities how my search might pan out. I might waste a lot of time browsing around on the internet and trawling through various sites hoping to strike gold. I might look for recommendations from other people.

But if there was a site or app which could recommend me books based on what I have read previously, that would be a massive help. Instead of wasting time on various sites, I could just log in and voila! 10 recommended books tailored to my taste.

This is what recommendation engines do and their power is being harnessed by most businesses these days. From Amazon to Netflix, Google to Goodreads, recommendation engines are one of the most widely used applications of machine learning techniques.

This time we bring to you another hackathon to apply your machine learning skills to build a recommender system and evaluate your skills in this subfield.

FAQs

1. Where can I find the dataset and the problem statement for the hackathon?

The contest will go live on the designated contest start date and time. There is a timer that is shown at the top of this page which shows the remaining time before the contest goes live. This is when you can access the problem statement and datasets from the problem statement tab.

2. Can I share my approach/code?

Absolutely. You are encouraged to share your approach and code file with the community. There is even a facility at the leaderboard to share the link to your code/solution description.

3. I am facing a technical issue with the platform/have a doubt regarding the problem statement. Where can I get support?

Join the AV slack channel by clicking on 'Join Slack Live Chat' button and ask your query at channel: janata_hack