MLS-C01 Question 10
Select 3You are building a recommendation system for a travel booking platform. The system needs to suggest personalized vacation packages based on users' preferences, past bookings, and location. Which of the following data sources would be most relevant for training the recommendation model?
- A
Users' past booking history, including destinations and travel dates
- B
Real-time weather data for potential travel destinations
- C
Users' demographic data, such as age, gender, and income
- D
Social media trends about popular vacation spots
- E
Geolocation data from users' mobile devices
Show answer and explanation
Correct answers: A, C, E
Explanation
To build a personalized recommendation system, it's essential to focus on primary data sources that capture users' preferences and contextual information. Past booking history, demographic data, and geolocation data are strong indicators of user preferences and can significantly improve the model's ability to recommend relevant vacation packages. Secondary data sources like weather or social media trends may provide supplementary insights but are not as critical for training the model.
- A. Correct.
This is correct. Users' past booking history is a primary source of information that reflects their preferences and behavior, making it crucial for training a personalized recommendation model.
- B. Incorrect.
This is incorrect. While weather data may be useful for real-time recommendations, it is not directly relevant for training a model focused on user preferences.
- C. Correct.
This is correct. Demographic data provides additional context about users, which can help personalize recommendations based on shared characteristics or trends.
- D. Incorrect.
This is incorrect. Social media trends might help identify overall popular destinations but are not specific to individual user preferences for training a recommendation model.
- E. Correct.
This is correct. Geolocation data provides context about users' current location, which can be used to tailor recommendations based on proximity or regional preferences.