Topic Vector Space Digital Magazine Recommendation
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Solution Overview
Problem
Existing digital content recommendation systems fail to provide users with meaningful content on topics likely to be of interest, especially when the content has not been accessed via the online system, and manually curated content does not accommodate dynamically changing user interests.
Innovation Solution
A digital magazine server scores digital magazines based on their relevance to topics by extracting topics from content items, generating magazine vectors, and ranking them in a topic vector space, allowing for the selection and recommendation of top magazines related to user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation techniques based on previous user interactions are used, then content can be recommended to users, but the recommendations fail to present meaningful content on topics likely to be of interest to users, especially content not accessed via the online system
Solution Approach 1:
The system segments the broad digital content into discrete topics by extracting topics from content items within each digital magazine. Each topic is represented as a separate vector in a topic vector space, allowing precise measurement of relevance between magazines and topics while maintaining coverage of diverse user interests.
Solution Approach 2:
The patent transforms the recommendation problem from a flat interaction-based approach to a multi-dimensional topic vector space. By representing magazines and topics as vectors with multiple dimensions, the system can measure relevance across numerous topic dimensions simultaneously, improving both accuracy and coverage of recommendations.
2Adaptability or versatility
If manually curated digital magazine cover pages are used, then content of interest to users can be presented, but the system fails to accommodate dynamically changing interests of the user and diverging topics in the digital content items
Solution Approach 1:
The system automatically performs topic extraction and vector generation for each digital magazine without manual intervention. The automated topic modeling approach enables the system to adapt to dynamically changing user interests and diverse content topics while reducing the complexity associated with manual curation processes.
Solution Approach 2:
The patent changes the parameters of the recommendation system from static manual curation to dynamic automated topic modeling. By using vector representations and similarity calculations, the system can rapidly adapt to changing user interests and content diversity without the complexity of manual re-curation.
3Productivity
If broad and unfiltered digital content is provided to users, then users have access to diverse content, but users can be overwhelmed by the content available
Solution Approach 1:
The system extracts topics from content items within each digital magazine to create topic vectors. This extraction process filters the broad content by identifying and isolating key topics, allowing the system to efficiently deliver relevant content while preventing information overload through targeted recommendations.
Solution Approach 2:
The patent replaces manual content filtering with automated topic modeling and vector-based similarity calculations. This substitution enables efficient processing of broad content at scale, delivering personalized recommendations without overwhelming users, as the automated system handles the filtering complexity.
Data Source
AI summary
A digital magazine server scores digital magazines based on how related the digital magazines are to each of a set of topics in a topic vector space, which allows ranking for each topic the magazines that are most closely related to the topic. The digital magazine server generates a magazine vector for the magazine in the topic vector space by aggregating extracted topics for the magazine and compares the magazine vector to each of the topics to determine a magazine-topic relevance score that indicates the relevance of the digital magazine to each topic in the topic vector space. The digital magazine server then ranks each of the digital magazines by their magazine-topic relevance scores for a particular topic, e.g., a trending topic from a user request, and selects a top number of magazines for the trending topic for the user.