Requirements

Let's refine our suggestion system. Imagine we're building a system that suggests items to users based on their past interactions and preferences. This isn't about ranking search results; it's about proactively suggesting items a user *might* be interested in, even if they haven't explicitly searched for them. Think "Recommended for You" sections on e-commerce sites or suggested videos on streaming platforms. The core idea is to analyze user behavior (views, purchases, likes, etc.) to identify patterns and similarities, and then suggest items that other similar users have interacted with positively. This update will add the capability to filter suggestions based on a complex set of criteria that can be defined and adjusted at runtime, and to support different similarity metrics. We also want to make the system easily extensible with new types of interactions and suggestion strategies. # Requirements ## Functional Requirements 1. **User Interaction Tracking:** * Support tracking various user interactions with items (e.g., `VIEW`, `PURCHASE`, `LIKE`, `ADD_TO_CART`, `BOOKMARK`). Each interaction should include the user ID, item ID, timestamp, and interaction type. * Allow adding and retrieving user interaction history. 2. **User Similarity Calculation:** * Implement different similarity metrics for calculating the similarity between users based on their interaction history (e.g., Jaccard index, cosine similarity, Pearson correlation). * Provide a mechanism to select the similarity metric to be used at runtime. * Define a configurable threshold for similarity. Only users with a similarity score above the threshold should be considered "similar". 3. **Suggestion Generation:** * Generate item suggestions for a given user based on the interactions of similar users. * Implement different suggestion strategies (e.g., "most popular among similar users," "items with the highest average rating from similar users," "items the target user has never interacted with"). * Provide a mechanism to select the suggestion strategy at runtime. * Limit the number of suggestions returned. * Handle the case where no similar users are found. 4. **Filtering Suggestions:** * Support filtering suggestions based on a flexible set of criteria. * Allow defining filter criteria at runtime using a rules engine or similar mechanism. Examples of filter criteria include: * Item category * Price range * Availability * Specific attributes (e.g., brand, color, size) * Allow combining multiple filter criteria using logical operators (AND, OR, NOT). 5. **Extensibility:** * The system should be easily extensible to support new interaction types, similarity metrics, suggestion strategies, and filter criteria without modifying existing code. ## Non-Functional Requirements 1. **Modularity:** The system should be composed of loosely coupled modules with well-defined interfaces. 2. **Extensibility:** The design should facilitate the addition of new features (e.g., new similarity metrics, suggestion strategies, filter criteria) with minimal impact on existing code. Favor composition over inheritance to enhance flexibility. 3. **Testability:** The code should be written in a way that makes it easy to write unit tests and integration tests. Dependencies should be easily mockable. 4. **Thread Safety:** Handle concurrent requests for suggestions from multiple users safely. Pay attention to read-write access on shared data structures (e.g., interaction history). Consider using appropriate synchronization mechanisms (e.g., locks, concurrent collections) where necessary. Aim for minimal contention to ensure performance. 5. **Performance:** Focus on efficient algorithms for similarity calculation and suggestion generation. Consider using appropriate data structures (e.g., indexes) to optimize query performance. Optimize common operations to reduce computational complexity, e.g., use caching or memoization if relevant. 6. **Maintainability:** The code should be well-documented and easy to understand. Follow coding conventions and SOLID principles. 7. **Configuration:** Allow configuring the system via external configuration files or a database. This should include the ability to specify the similarity metric, suggestion strategy, filter criteria, similarity threshold, and the number of suggestions to return. ## Core Entities 1. **User:** Represents a user of the system. Contains a unique user ID and potentially other user-related information (e.g., profile details). 2. **Item:** Represents an item in the system. Contains a unique item ID and potentially other item-related information (e.g., name, category, price). 3. **Interaction:** Represents a user's interaction with an item. Contains the user ID, item ID, interaction type (e.g., VIEW, PURCHASE, LIKE), and timestamp. 4. **InteractionType:** An enum representing the type of interaction between a user and an item. 5. **SimilarityMetric:** An interface defining the contract for calculating the similarity between two users. Implementations include Jaccard index, cosine similarity, and Pearson correlation. 6. **SuggestionStrategy:** An interface defining the contract for generating item suggestions for a user. Implementations include "most popular among similar users," "items with the highest average rating from similar users." 7. **FilterCriteria:** Represents a single filter criterion for filtering suggestions. 8. **Filter:** An interface for applying filter criteria to a list of suggestions. This could use a Strategy pattern to allow different filtering implementations. 9. **Suggestion:** Represents a suggested item for a user. Contains the item ID and potentially a score indicating the relevance of the suggestion. 10. **SuggestionEngine:** The main class responsible for orchestrating the suggestion process. It manages user interactions, calculates user similarity, generates suggestions, and applies filters.

Design a suggestion system

Requirements

Interview Simulation

Functional Requirements

💡 Interview Tip

Non-Functional Requirements

Core Entity Identification

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