Requirements

Let's design the core of an online music recommendation system. Think of popular music streaming services: they analyze user listening habits, preferences, and song metadata to suggest songs a user might enjoy. We're not building the entire streaming service, just the recommendation engine's core logic. This system will take user listening history and song information as input and generate a list of recommended songs. The challenge is to create a flexible and maintainable system that can easily adapt to new recommendation algorithms and data sources. # Requirements ## Functional Requirements 1. **User Registration and Profile Management:** * The system must allow users to register with basic information (user ID, name). * The system must store user profiles, including listening history (list of songs listened to with timestamps and ratings if available), preferred genres, artists, and other relevant metadata. We should simulate this: no actual database interactions. * The system must allow retrieving user profiles. 2. **Song Management:** * The system must allow adding and managing songs, including metadata like song ID, title, artist, genre, and other relevant attributes. * The system should allow retrieving songs by ID or other metadata. 3. **Recommendation Generation:** * The system must implement at least two different recommendation algorithms: * **Collaborative Filtering:** Recommend songs based on the listening history of similar users. Similarity can be defined by genre preferences, listening history overlap, etc. * **Content-Based Filtering:** Recommend songs based on the metadata of songs the user has liked or frequently listened to. * The system must allow selecting which recommendation algorithm to use for a given user. * The system must be able to combine the results of multiple recommendation algorithms (e.g., weighted average of scores). * The system must generate a list of recommended song IDs, sorted by relevance (highest score first). 4. **Listening History Tracking:** * The system must allow tracking user listening history, including song ID, timestamp, and optional rating. * The system must update user profiles with new listening data. 5. **Algorithm Configuration:** * The system should allow configuration of recommendation algorithms. Examples: * Adjusting the weight of different factors in content-based filtering (e.g., genre vs. artist). * Setting the minimum number of similar users required for collaborative filtering. ## Non-Functional Requirements 1. **Extensibility:** The system should be easily extensible to support new recommendation algorithms, data sources, and user profile attributes. Consider the Open/Closed Principle. 2. **Modularity:** The system should be composed of loosely coupled modules, making it easier to understand, maintain, and test. 3. **Testability:** The system should be designed to be easily testable, with clear separation of concerns and well-defined interfaces. Unit tests should be feasible. 4. **Thread Safety:** The system might serve concurrent requests. Therefore, any shared data structures must be thread-safe, using appropriate synchronization mechanisms if needed. Think about scenarios involving multiple users accessing song information or updating their listening history simultaneously. Use concurrent data structures where applicable. 5. **Performance:** While not a primary focus for this LLD exercise, the recommendation generation process should be reasonably efficient. Avoid unnecessary computations and optimize data access patterns. Consider using appropriate data structures for efficient lookups (e.g., HashMaps for song IDs). ## Core Entities 1. **User:** Represents a user of the music recommendation system. Attributes include user ID, name, and listening history. 2. **Song:** Represents a song in the system. Attributes include song ID, title, artist, genre, and other metadata. 3. **UserProfile:** Encapsulates a user's profile information, including listening history, preferences, and other relevant data. 4. **RecommendationAlgorithm:** An interface representing a recommendation algorithm. Implementations include CollaborativeFiltering and ContentBasedFiltering. 5. **RecommendationEngine:** Orchestrates the recommendation process, selecting algorithms, combining results, and ranking recommendations. 6. **SongMetadataService:** A service responsible for retrieving and managing song metadata. 7. **UserListeningHistoryService:** A service responsible for tracking and managing user listening history. 8. **SimilarityCalculator:** An interface for calculating the similarity between users or songs. Implementations might include genre-based similarity or listening history overlap.

Create an online music recommendation system which suggests songs according to user taste.

Requirements

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