Multi-Feature Song List Generation for Consistent Recommendations

The patent transitions from single-dimension feature grouping to multi-dimensional feature space comparison. By representing songs as vectors in a high-dimensional space where each dimension corresponds to a different musical feature (tempo, genre, mood, etc.), the system achieves more comprehensive and accurate song matching while maintaining computational feasibility through vector operations.

The patent changes the parameter representation from discrete categorical features to continuous vector embeddings. This transformation allows for gradient-based optimization and enables the use of similarity metrics like cosine similarity, which provide smooth and nuanced comparison across multiple dimensions, thereby improving song list consistency.

The patent employs multi-dimensional feature vectors to capture songs from multiple aspects simultaneously (tempo, genre, mood, instrumentation, etc.). This dimensional expansion enables more accurate recommendation by considering the holistic similarity between songs rather than relying on a single feature, thus improving recommendation accuracy while maintaining efficient vector-based computation.

The patent performs preliminary action by pre-computing and storing feature vectors for all songs in the database. This preprocessing step transforms raw audio and metadata into standardized vector representations in advance, enabling fast similarity computation during recommendation generation without sacrificing accuracy, thereby resolving the speed-accuracy tradeoff.