Improve the user experience on a fast-growing mobile platform, for hosting coding challenges - Recommendation Challenge
Your client is a fast-growing mobile platform, for hosting coding challenges. They have a unique business model, where they crowdsource problems from various creators(authors). These authors create the problem and release it on the client's platform. The users then select the challenges they want to solve. The authors make money based on the level of difficulty of their problems and how many users take up their challenge.
The client, on the other hand, makes money when the users can find challenges of their interest and continue to stay on the platform. Till date, the client has relied on its domain expertise, user interface and experience with user behavior to suggest the problems a user might be interested in. You have now been appointed as the data scientist who needs to come up with the algorithm to keep the users engaged on the platform.
The client has provided you with a history of last 10 challenges the user has solved, and you need to predict which might be the next 3 challenges the user might be interested to solve. Apply your data science skills to help the client make a big mark in their user engagements/revenue.

Dataset description:
We have three data files,

• Train.csv
  

Variable Definition
user_sequence Unique ID for the sequence
user_id USer ID
challenge _sequence Challenge sequence number(1-13)
challenge Challenge ID

• Challenge.csv
  Variable Definition
  Challenge_ID Challenge_ID
  programming _language Programming language for the challenge
  

challenge_series _ID Series for the given challenge
total_submissions Total submission by all users
publish_date Publishing date for the challenge
author_ID Author ID
author_org_ID Organization ID for the author
category_id Type of challenge

Evaluation Metric

• The evaluation metric is Mean Average Precision (MAP) at K (K = 3). MAP is a well-known metric used to evaluate ranked retrieval results. E.g. Let’s say for a given user, we recommended 3 challenges and only 1 st and 3 rd challenges are correct. So, the result would look like — 1, 0, 1
  
• In this case, The precision at 1 will be: 11/1 = 1 The precision at 2 will be: 01/2 The precision at 3 will be: 1*2/3 = 0.67 Average Precision will be: (1 + 0 + 0.67)/3 = 0.556.