Databricks Exam Databricks-Certified-Professional-Data-Scientist Topic 6 Question 40 Discussion

Actual exam question for Databricks's Databricks-Certified-Professional-Data-Scientist exam

Question #: 40
Topic #: 6

[All Databricks-Certified-Professional-Data-Scientist Questions]

You are creating a model for the recommending the book at Amazon.com, so which of the following recommender system you will use you don't have cold start problem?

ANaive Bayes classifier

BItem-based collaborative filtering

CUser-based collaborative filtering

DContent-based filtering
The cold start problem is most prevalent in recommender systems. Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages) that are likely of interest to the user. Typically, a recommender system compares the user's profile to some reference characteristics. These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach). In the content-based approach, the system must be capable of matching the characteristics of an item against relevant features in the user's profile. In order to do this, it must first construct a sufficiently-detailed model of the user's tastes and preferences through preference elicitation. This may be done either explicitly (by querying the user) or implicitly (by observing the user's behaviour). In both cases, the cold start problem would imply that the user has to dedicate an amount of effort using the system in its 'dumb' state - contributing to the construction of their user profile - before the system can start providing any intelligent recommendations.
Content-based filtering recommender systems use information about items or users to make recommendations, rather than user preferences, so it will perform well with little user preference data. Item-based and user-based collaborative filtering makes predictions based on users' preferences for items, os they will typically perform poorly with little user preference data. Logistic regression is not recommender system technique.

Show Suggested Answer

Suggested Answer: C

by My at Mar 28, 2023, 02:28 PM

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Marcos

11 days ago

I'm not sure, but I think A) Naive Bayes classifier could also work well in this scenario.

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Temeka

12 days ago

I agree with Dierdre. Content-based filtering seems like the best option to avoid the cold start problem.

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Dierdre

21 days ago

I think I would go with D) Content-based filtering because it doesn't require much user preference data.

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