Restaurant reservation company has introduced a recommendation engine on its platform to suggest restaurants to users. The company wishes to evaluate the effectiveness of these recommendations and optimize them for better user engagement.
User_ID: Unique identifier for each user.Age_Group: Age category the user falls into.Preferred_Cuisine: The type of cuisine the user prefers based on past reservations and searches.Last_Visited_Restaurant: The most recent restaurant the user visited.Recommendation_Clicked: Indicates if the user clicked on the restaurant recommendation provided by company (YesorNo).Reservation_Made: Indicates if the user made a reservation after clicking on the recommendation (YesorNo).Visit_Rating: Rating is given by the user for the restaurant they visited through the recommendation. 'NA' if no reservation was made.
- Determine the overall success rate of the recommendation engine in terms of user clicks and reservations made.
- Analyze the effectiveness of recommendations based on the
Age_GroupandPreferred_Cuisine. Identify any patterns or segments where recommendations are particularly successful or lacking. - Based on the
Visit_Rating, evaluate the satisfaction of users with the recommended restaurants. - Analyze the data to deduce if the name or type of the
Last_Visited_Restauranthas any impact on users trusting the next recommendation. For instance, do users who last visited a seafood restaurant trust a seafood recommendation more? - Provide suggestions on how company can improve its recommendation algorithm to better cater to user preferences and improve reservation rates.