Databricks Certified Professional Data Scientist Exam - 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.

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Suggested Answer: C

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

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Alayna

5 months ago

Wow, I didn’t realize cold start was such a big deal for collaborative filtering!

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Dorethea

5 months ago

I’m not convinced, user-based could still work if you have enough data.

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Rene

6 months ago

Wait, why not content-based filtering? Seems like a solid option too.

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Erinn

6 months ago

Totally agree, it uses existing data effectively!

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Julio

6 months ago

I think item-based collaborative filtering is the best choice here.

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Peggie

6 months ago

I feel like Naive Bayes classifier isn't really a recommender system method, but I can't recall why exactly.

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Daron

6 months ago

I practiced a similar question, and I believe user-based collaborative filtering struggles with new users, so it’s probably not the right answer.

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Kristel

6 months ago

I'm a bit unsure, but I remember that item-based collaborative filtering needs more data to work effectively, so it might not be ideal for cold starts.

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Carin

6 months ago

I think content-based filtering might be the best choice here since it doesn't rely on user preferences as much.

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Catalina

6 months ago

I'm pretty confident this one. I think the answer is B - the administrator can generate a Tenant report from within the Deep Security Manager Web console.

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Sharmaine

6 months ago

This question seems pretty straightforward. I think the key is to focus on the differences between blanket orders and regular purchase orders.

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Sheridan

11 months ago

If the cold start problem had a face, I'd recommend it a good book. Maybe 'How to Thaw Out Your Personality' or 'Warming Up to Recommendations 101'.

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Celeste

11 months ago

D) Content-based filtering is the way to go here. Who needs user preferences when you've got good old-fashioned content analysis? Screw the cold start, I'm bringing the heat!

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Jettie

10 months ago

D) Content-based filtering

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Britt

10 months ago

C) User-based collaborative filtering

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Pete

10 months ago

B) Item-based collaborative filtering

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Adolph

10 months ago

A) Naive Bayes classifier

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Felix

11 months ago

A) Naive Bayes classifier? Really? That's not even a recommender system technique. I'm going to go with D) Content-based filtering on this one.

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Nida

10 months ago

I'm going to go with D) Content-based filtering on this one.

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Lillian

10 months ago

Naive Bayes classifier? Really? That's not even a recommender system technique.

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Loreen

11 months ago

Hmm, I'm torn between B) Item-based collaborative filtering and D) Content-based filtering. Both of these seem like they could work well without the cold start problem.

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Casie

10 months ago

I agree with both of you. B) Item-based collaborative filtering and D) Content-based filtering are both good choices for avoiding the cold start problem.

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Catina

11 months ago

I think B) Item-based collaborative filtering is the way to go. It focuses on similarities between items, so it should also work without the cold start problem.

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Terrilyn

11 months ago

I would go with D) Content-based filtering. It doesn't rely on user preferences, so it should work well without the cold start problem.

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Justine

11 months ago

I think the correct answer is D) Content-based filtering. Since the question mentions the cold start problem, content-based filtering would be the best option as it doesn't rely on user preferences like collaborative filtering.

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