Content Recommendation System Using Vector Similarity Analysis
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Solution Overview
Problem
Existing personalized content recommendation services lack reliability as they rely on indirect relationships between users and content, requiring extensive purchase/use histories and similar user patterns, which limits their effectiveness.
Innovation Solution
A content recommendation system that creates content vectors from meta and review information, and user vectors from consumption history, allowing for direct content recommendations based on similarity analysis, without needing extensive user data or similar user patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing personalized recommendation service is used based on user purchase/use history and similar user patterns, then content can be recommended to users, but the recommendation reliability is low and extensive user data is required
Solution Approach 1:
The patent introduces an intermediary entity (the similarity calculation module and content matching module) that bridges the user and content directly through vector representation. Instead of relying on other users as intermediaries (collaborative filtering), the system creates a direct pathway by converting both user preferences and content characteristics into comparable vector forms, enabling reliable recommendations even with limited user data.
Solution Approach 2:
The patent transforms the recommendation problem from analyzing raw purchase history data to comparing vector representations. By changing the parameter space from categorical user behavior data to continuous vector space with similarity metrics, the system can reliably recommend content based on vector similarity rather than requiring extensive historical data for pattern recognition.
2Reliability
If collaborative filtering method is used to find users with similar purchase/use patterns, then content can be recommended, but the direct relationship between user and recommended content is lost
Solution Approach 1:
The patent extracts the essential characteristics of user preferences and content attributes into vector representations, separating the core similarity measurement from the complex process of finding similar users. This extraction allows direct comparison between user vectors and content vectors, establishing a direct relationship while simplifying the recommendation process by removing the intermediate step of user-user similarity calculation.
Solution Approach 2:
The patent transitions from the traditional user-user collaboration dimension to a user-content vector similarity dimension. By representing both users and contents in the same vector space, the system enables direct comparison across this new dimension, establishing direct relationships without the complexity of multi-step collaborative filtering processes.
Data Source
AI summary
Disclosed herein are a system for content recommendation service, a content recommendation device and a method of operating the same. The system can recommend personalized content directly related with a recommendation target, irrespectively of whether the content consumption history of the recommendation target user is sufficient to analyze the purchase/use pattern or whether there are other users having similar purchase/user pattern with the recommendation target user. As a result, the recommendation reliability can be improved.