Collaborative Filtering for Content Recommendations
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Solution Overview
Problem
Content publishers face challenges in filtering and recommending relevant content to users in a rapidly changing electronic content landscape, leading to decreased user engagement and revenue, as existing methods fail to effectively identify and prioritize content that is likely to interest users.
Innovation Solution
A system and method for collaboratively filtering content recommendations using user activity data to identify document connection pairs, grading these connections based on common user, aging, and causality components, and providing filtered recommendations that are likely to interest users, thereby enhancing user engagement and publisher revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content publishers filter the massive pool of possible content recommendations using existing methods, then the filtering process becomes manageable, but the recommendations fail to effectively identify and prioritize content that is likely to interest users
Solution Approach 1:
The patent segments the content recommendation problem into multiple independent grading components: common user grade component (based on user overlap), aging grade component (based on content recency), and causality grade component (based on temporal relationships). Each component is calculated separately and then combined to produce an overall grade, allowing precise and efficient filtering of content recommendations.
2Adaptability or versatility
If the system processes large datasets of user activity data to identify document connection pairs, then personalized recommendations can be provided, but the computational complexity and processing time increase
Solution Approach 1:
The patent divides the complex computational task into separate grading components that can be independently calculated and combined. The common user grade component, aging grade component, and causality grade component are each computed using distinct algorithms, reducing the overall computational complexity while maintaining personalization capabilities.
Solution Approach 2:
The system pre-computes user activity data and document connection pairs before generating recommendations. By preparing this data in advance and organizing it into structured formats, the system reduces real-time computational requirements when actually generating personalized recommendations.
3Reliability
If the system recommends popular content based on high user engagement, then the recommendations are likely to be well-received, but bias from popularity reduces the diversity and relevance of recommendations
Solution Approach 1:
The patent applies different weighting factors to different grading components based on local conditions. The causality grade component can be adjusted to emphasize or de-emphasize popular content depending on the specific recommendation context, allowing the system to balance popularity with diversity and relevance for each individual recommendation scenario.
Data Source
AI summary
Identifying collaboratively filtered content recommendations based on user activity data collected from users in an electronic environment is disclosed. The user activity data includes information relating to user-engagement indications of the users. Multiple connection pairs are identified based on the user activity data, with each connection pair including a potential target document and a candidate recommendation document. A connection strength is determined for each of the identified connection pairs. An overall grade is generated for a set of candidate connection pairs of the plurality of connection pairs, wherein the overall grade is based on the respective connection strength associated with each of the set of candidate connection pairs. One or more filtered recommendations for provisioning in connection with the target document based on a comparison of the overall grade associated with each of the set of candidate connection pairs.