Personalized Content Blending for Multi-Slot Recommendation
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Solution Overview
Problem
Existing methods for personalized content blending from heterogeneous corpora are not data-driven and lack personalization, as they rely on fixed ratios or calibration approaches that are not scalable and do not account for user preferences, leading to suboptimal user satisfaction.
Innovation Solution
A method and system that utilize a multi-armed contextual bandit (MA-CB) model to learn a reward predictor from user feedback, employing a greedy slotting technique to optimize content allocation on webpages based on personalized scores and business rules, addressing the challenges of heterogeneous content aggregation and user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed ratio approach is used to blend content items from different corpora, then the blending process is simple and deterministic, but it does not provide personalization and leads to suboptimal user satisfaction
Solution Approach 1:
The patent employs feedback mechanisms by using user interactions (clicks, views, engagement metrics) to continuously learn and update user preference models. This allows the system to adapt content blending ratios dynamically based on actual user behavior, resolving the contradiction between simple blending and personalization by making the blending process adaptive rather than static.
Solution Approach 2:
The system transitions from fixed, static blending ratios to dynamic, adaptive blending ratios that change based on user preferences and context. By implementing machine learning models that continuously learn from user feedback, the system makes the blending process dynamic, allowing it to adapt to individual user needs while maintaining operational feasibility through automated decision-making.
2Measurement precision
If a calibration approach is used to align recommendation scores from different corpora, then score comparability is improved, but scalability is reduced and ground truth availability is required
Solution Approach 1:
The patent changes the fundamental parameters used for score calibration by transitioning from ground-truth-based calibration to preference-based scoring. Instead of requiring external ground truth labels, the system uses user preference signals to learn and adjust scoring parameters across different corpora, enabling scalable personalization without relying on unavailable ground truth data.
Solution Approach 2:
The system introduces a preference model as an intermediary that mediates between different content corpora and user preferences. This preference model learns to translate and compare scores from different corpora based on user feedback, eliminating the need for direct ground truth calibration while maintaining score comparability through the learned preference representations.
3Reliability
If dedicated models are used for each content corpus, then content recommendation accuracy for each corpus is improved, but merging heterogeneous contents into a single result set becomes challenging
Solution Approach 1:
The patent implements a universal preference model that serves multiple functions: it learns from user feedback, ranks content across different corpora, and adapts to various content types. This multi-functional model eliminates the need for separate blending mechanisms for each corpus pair, reducing overall system complexity while maintaining high recommendation accuracy through unified preference-based ranking.
4Ease of manufacture
If traditional blending approaches are used, then implementation is straightforward, but user satisfaction and engagement are suboptimal due to lack of personalization
Solution Approach 1:
The system implements self-service by using automated machine learning models that continuously learn from user feedback and automatically adjust content blending strategies. This eliminates the need for manual configuration and tuning, maintaining implementation simplicity while dramatically improving user satisfaction through continuous adaptive personalization based on actual user behavior.
Data Source
AI summary
The present teaching relates to personalized content recommendation. A webpage is contrasted for a user having a plurality of slots each of which is to be allocated with a content item. For each of the plurality of slots, a plurality of content items in a plurality of types of content are accessed. For each of the plurality of types of content, a personalized score is predicted for each content item in the type of content, wherein the personalized score is obtained based on a trained model trained. A recommended content item of the type of content is selected based on personalized scores. An overall recommended content item is selected and allocated to a slot based on criteria associated with the personalized scores of the recommended content items and a business rule. The webpage with the plurality of slots allocated with content items is provided to the user.