Engagement Predictor Using Semantic Embeddings for Content Interest
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Solution Overview
Problem
Conventional click prediction models struggle to accurately predict user interest in arbitrary documents without relying on historical browsing data or click logs, limiting their applicability in scenarios with little or no prior user interaction.
Innovation Solution
The Engagement Predictor employs high-level semantic concept mappings to construct models that predict user interest by analyzing transitions between source and destination content, using features like geotemporal and latent semantic features, and applies these models to arbitrary documents to identify 'interesting nuggets' without requiring browsing history or click logs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional click prediction models use historical CTR and browsing data, then prediction accuracy is improved, but applicability to arbitrary documents without prior user interaction is reduced
Solution Approach 1:
The patent introduces document embeddings as an intermediary representation that captures semantic meaning of arbitrary documents without requiring historical interaction data. These embeddings serve as a bridge between the document content and the prediction model, enabling the system to process any document while maintaining prediction accuracy through learned semantic features rather than reliance on historical CTR data.
Solution Approach 2:
The patent transforms the input parameters from traditional click prediction by using document embeddings that capture semantic features instead of relying on historical browsing data and CTR metrics. This parameter transformation allows the model to adapt to arbitrary documents by changing the feature representation from interaction-based to semantics-based, resolving the contradiction between accuracy and versatility.
2Ease of operation
If click prediction models rely on ad query and query augmented metadata, then personalized and sponsored search is improved, but direct applicability to arbitrary documents is lost
Solution Approach 1:
The patent creates a universal document embedding framework that can handle multiple types of documents and search scenarios simultaneously. The same embedding mechanism works for both sponsored search with ad queries and arbitrary document processing, making the system multi-functional. This universal approach eliminates the need for separate models for different document types while maintaining personalized search capabilities through semantic understanding.
3Productivity
If contextual advertising models match page content with ad subject, then ad revenue optimization is improved, but ability to predict browsing transitions from arbitrary pages is reduced
Solution Approach 1:
The patent replaces the mechanical keyword-matching system used in contextual advertising with a semantic embedding-based approach. Instead of mechanically matching ad subjects with page content keywords, the system uses learned document embeddings to capture semantic relationships. This substitution enables the model to predict browsing transitions from arbitrary pages by understanding semantic meaning rather than relying on exact keyword matches, thus improving versatility while maintaining ad revenue optimization capabilities.
Data Source
AI summary
An “Engagement Predictor” provides various techniques for predicting whether things and concepts (i.e., “nuggets”) in content will be engaging or interesting to a user in arbitrary content being consumed by the user. More specifically, the Engagement Predictor provides a notion of interestingness, i.e., an interestingness score, of a nugget on a page that is grounded in observable behavior during content consumption. This interestingness score is determined by evaluating arbitrary documents using a learned transition model. Training of the transition model combines web browsing log data and latent semantic features in training data (i.e., source and destination documents) automatically derived by a Joint Topic Transition (JTT) Model. The interestingness scores are then used for highlighting one or more nuggets, inserting one or more hyperlinks relating to one or more nuggets, importing content relating to one or more nuggets, predicting user clicks, etc.