Ramgopal Prajapat:

Learnings and Views

Recommendation Engine - Questions

By: Ram on Oct 25, 2020

Q1: Finding similarity is one of the key steps in the Recommendation Engine Algorithm. Please name the algorithms which are related to each of the Item-Item and User-User similarity.

 

Q2: Recommendation Algorithms drives key business objectives. Can you please name at least 4 business measures it can impact?

 

Q3: Can you please name 3 distance measures that are used in Recommendation Algorithms?

 

Q4: In movie recommendations, user rating data is used for finding similarity. Assume that in your context, the user-ratings are not available or you want to augment the data to improve performance. What type of data can you use to augment the data available for similarity measurement?

 

Q5: Except for the known scenarios (Product, connection. video, or music) scenarios of recommendations. Can you please list 4 new scenarios of recommendation applications?

 

Q6: Identify the correct statements related to Collaborative Filtering

  1. The problem of collaborative filtering is to predict how well a user will like an item that he has not rated given a set of historical preference judgments for a community of users
  2. Predict the opinion the user will have on the different items
  3. Recommend the ‘best’ items based on the user’s previous likings and the opinions of like-minded users whose ratings are similar
  4. None of these

Q7: Which option of these are the challenges with Collaborative Filtering. Multiple Answers could be correct.

  1. Scalability
  2. Diversity
  3. Data sparsity
  4. Shilling attacks

Q8: Which of these options are correct about the collaborative filtering algorithm? Multiple Answers could be correct.

  1. User-Based
  2. Item Based
  3. Memory Based
  4. Deep Learning Based

Q9: An Analytica Kitchens approached you and asked you to build a recommendation engine based process to recommend dishes to their customers. Which algorithm would you use and why?

 

Q10: You are working as a Data Scientist with a content provider like Analytics/Data Science points of view and blogs. They have a subscription-based business model. You have to build a recommendation engine based model to show the list of the article relevant to the users. What type of features would you use and which algorithm may be more appropriate?

 

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