Design Composable Event Recommendations

Problem Statement

You are building a recommendation engine for a ticket marketplace. Users have specific preferences, and events must be filtered and ranked based on these rules. You need to implement a system that evaluates an Event against a collection of Filter and Ranker strategies.

An Event is represented as follows:

class Event:
    def __init__(self, id, category, price, popularity_score, start_time):
        self.id = id
        self.category = category
        self.price = price
        self.popularity_score = popularity_score
        self.start_time = start_time

Tasks:

1. Define an interface or abstract base class for Filter (e.g., CategoryFilter, PriceRangeFilter).
2. Define an interface for Ranker (e.g., PopularityRanker, PriceRanker).
3. Implement a RecommendationEngine class that takes a list of Event objects, applies all filters, and then sorts the remaining events based on a weighted combination of active rankers.

Example:

Input: A list of 3 events. Filter: CategoryFilter('Sports'). Ranker: PopularityRanker.

• Event A: {'id': 1, 'category': 'Sports', 'price': 50, 'popularity_score': 0.8}
• Event B: {'id': 2, 'category': 'Music', 'price': 30, 'popularity_score': 0.9}
• Event C: {'id': 3, 'category': 'Sports', 'price': 100, 'popularity_score': 0.7}

Output: [Event A, Event C] (filtered by 'Sports' and sorted by popularity).

Constraints:

• Use clean, extensible code (follow the Open/Closed Principle).
• Handle the case where no events match the filters.
• Ensure the ranking logic can handle multiple rankers simultaneously (e.g., a weighted score of popularity and price).