Content Recommendation System Using Dual-Set Ranking
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Solution Overview
Problem
Conventional content recommendation systems rely on short-term user interactions, such as click-through rates, which fail to capture long-term user interests and often result in incomplete and reactive content suggestions, limiting the discovery of new interests and failing to provide personalized content across multiple applications.
Innovation Solution
A method that generates two sets of candidate content items based on user profiles and click likelihood, using learning models to rank and present content recommendations, incorporating user activity vectors to estimate the likelihood of content item engagement and combining user-profile and user-activity based selections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content recommendation systems rely on short-term user interactions (e.g., click-through rates), then the system can quickly identify and recommend content matching current user preferences, but the system fails to capture long-term user interests and creates a personalization bubble that limits discovery of new interests
Solution Approach 1:
The patent segments user interests into multiple dimensions including short-term interests, long-term interests, and latent interests. This segmentation allows the system to process and recommend content across different time horizons simultaneously, preventing over-reliance on any single interest type while maintaining recommendation speed through specialized processing for each segment.
Solution Approach 2:
The system performs preliminary actions by proactively exploring and identifying potential user interests before the user actively expresses them. Through mechanisms like diverse content exposure and interest inference, the system prepares recommendation candidates that go beyond observed behavior, enabling long-term interest capture without sacrificing immediate recommendation responsiveness.
2Ease of manufacture
If the system collects user interaction data passively, then data collection is simple and low-cost, but the system can only provide reactive services rather than proactive content discovery
Solution Approach 1:
The patent introduces an intermediary interest inference mechanism that bridges passive data collection and proactive service delivery. This intermediary layer processes observed interactions to infer underlying user interests and potentials, then uses these inferences to proactively recommend content that aligns with inferred interests rather than merely reacting to explicit user actions.
Solution Approach 2:
The system implements feedback loops where recommended content based on inferred interests generates new interaction data, which in turn refines the interest inference model. This feedback mechanism enables the system to progressively improve its proactive capabilities while maintaining simple passive data collection, as the feedback is processed through intelligent inference rather than requiring active data gathering.
3Adaptability or versatility
If each application creates its own content subset based on application-specific user interests, then the content selection is tailored to the application context, but the system cannot serve a broader range of user interests across multiple applications
Solution Approach 1:
The patent merges content pools across multiple applications by creating a unified interest representation that aggregates user interactions from different contexts. This unified model allows the system to maintain application-specific personalization through context-aware filtering while simultaneously leveraging the broader content pool accessible across applications, eliminating fragmentation without sacrificing contextual adaptability.
Solution Approach 2:
The system implements a universal interest model that serves multiple applications simultaneously. This multi-functional approach allows the same underlying interest inference engine to personalize content across different application contexts, reducing the need for separate content subsets while maintaining application-specific relevance through contextual parameters in the recommendation process.
Data Source
AI summary
Method, system, and programs for providing content recommendation are disclosed. A first set of candidate content items may be generated based on a user profile, and a second set of candidate items may be generated based on the likelihood that the user will click a corresponding candidate content item in the second set. The candidate content items in the first and second sets may be ranked together using a learning model and presented to the user as content recommendations based on their rankings. The likelihood that the user will click a given candidate content item in the second set may be estimated based on similarities between the given content item and content items related to the given content item. Such a similarity may be computed based on activities performed by users who have viewed both the given content item and a related content item.