User App Interest Embedding Model for Sparse Feature Representation

The patent transforms the feature representation from one-hot encoding (sparse high-dimensional vectors) to embedding vectors (dense low-dimensional vectors). This parameter change in representation dimensionality and density resolves the contradiction by maintaining sufficient feature space for distinguishing different apps while improving relationship expression capability through dense vector semantics.

The patent creates embedding vectors as compressed representations that copy and encode the essential characteristics of app installation features. Instead of using direct one-hot encoding, the system generates embedding vectors that capture app relationships and user interests in a more efficient dense format, resolving the information loss problem while reducing feature space requirements.

The patent changes the parameter of feature representation from discrete one-hot vectors to continuous embedding vectors. This allows the system to maintain precise app identification through unique embedding vectors while simultaneously expressing relationships between apps through vector similarity and semantic relationships in the embedding space.

The patent combines multiple sources of information (app ID, app category, user behavior patterns) into composite embedding vectors. These composite vectors simultaneously encode app identity and relationship information, resolving the contradiction by integrating multiple information dimensions into a unified representation that preserves both identification accuracy and relationship expression.

The patent transforms category statistical features into embedding vectors through learned transformations. This parameter change allows the system to maintain reduced feature dimensionality while capturing richer user interest information through the semantic relationships encoded in the embedding space, which statistical features alone cannot provide.

The patent introduces embedding vectors as an intermediary representation between raw app data and downstream tasks. These embedding vectors serve as a bridge that compresses information efficiently while preserving user interest preferences and app relationships, resolving the contradiction between dimensionality reduction and information retention.