Dynamic User Profile for Fast Content Recommendations
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Solution Overview
Problem
Conventional content recommendation systems are either static and user-preference insensitive, or they rely on collective user relevance, which may not align with individual user preferences, leading to suboptimal content suggestions.
Innovation Solution
A system and method that determine user profiles based on document fingerprints using a cosine product estimation, enabling dynamic and personalized content recommendations by updating user profiles with each document access, and employing a preprocess engine to calculate document fingerprints for efficient content ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional content-based filtering systems pre-compute content recommendations offline, then the system can provide static recommendations quickly, but the recommendations do not take user preferences into consideration and are insensitive to user behavior
Solution Approach 1:
The system pre-computes and stores document fingerprints (vector representations) for all documents in the database before they are needed for recommendations. This preliminary action allows the system to quickly compare user profiles against pre-processed document data without performing complex computations in real-time, thus achieving both fast recommendation generation and user preference adaptability
Solution Approach 2:
The system dynamically updates user profiles based on user behavior (documents viewed, search queries) while maintaining a static pre-computed document fingerprint database. This dynamic adaptation allows recommendations to evolve with user preferences without requiring re-computation of the entire recommendation system, resolving the contradiction between speed and adaptability
2Adaptability or versatility
If collaborative filtering systems use what other users find relevant to generate recommendations, then user preferences are considered, but the recommendations may not align with individual user preferences and require significant processing resources
Solution Approach 1:
The system extracts only the essential features of documents into compact vector fingerprints, removing unnecessary complexity from the original document data. This extraction allows the system to work with simplified representations that reduce computational complexity while still capturing the semantic content needed for personalized recommendations
Solution Approach 2:
The system introduces document fingerprints as an intermediary representation between raw document data and recommendation generation. These fingerprints serve as a mediator that bridges the gap between user profiles and document content, enabling efficient comparison and matching without requiring complex processing of the full document corpus
3Loss of time
If content recommendations are pre-computed and provided statically to users, then processing time is reduced, but the recommendations lack personalization and do not update with user behavior
Solution Approach 1:
The system performs preliminary computation of document fingerprints and stores them for rapid retrieval. This pre-processing eliminates the need for time-consuming full-text analysis during recommendation generation, while the separate dynamic user profile component ensures user preference information is preserved and applied to the pre-computed results
Solution Approach 2:
The system segments the recommendation problem into two independent components: static document fingerprints (pre-computed) and dynamic user profiles (updated based on behavior). This segmentation allows each component to be optimized separately - the document data for speed and the user data for personalization - while being combined to produce personalized recommendations efficiently
Data Source
AI summary
In various example embodiments, a system and associated method for fast computation content recommendation is provided. A dynamic user profile for a user is determined. The dynamic user profile is based on one or more fingerprints of viewed documents by the user. One or more relevant documents are determined based on the dynamic user profile and the fingerprints of documents in the system. A list of the one or more relevant documents is then provided to the user.