Content Recommendation System Using Dynamic User Profiling
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Solution Overview
Problem
Existing content recommendation systems are limited in their ability to accurately measure user preferences due to their reliance on narrow behavioral analysis and rigid content-type hierarchical structures, which degrades the quality of user profiles and fails to provide comprehensive recommendations for audiovisual content.
Innovation Solution
A method and system that create a more elaborate user profile by defining content fields and applying weights to user behaviors, and calculate content ratings using Advanced Television Systems Committee (ATSC) and TV-Anytime metadata, allowing for flexible associativity measurement across various hierarchical structures to recommend content based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If implicit profiling is used to continuously monitor user preferences without intervention, then user profile quality improves over time, but the system complexity increases due to the need for sophisticated behavior analysis models
Solution Approach 1:
The system segments user behavior analysis into multiple independent modules: explicit profiler, implicit profiler, and stereotype profiler. Each module handles specific aspects of user preference detection, allowing the complex system to be divided into manageable components that can be independently optimized and maintained.
Solution Approach 2:
The user profile is designed as a dynamic structure that continuously evolves based on observed user behavior. The system automatically updates profile parameters without requiring manual intervention, allowing the profile to adapt to changing user preferences over time while maintaining high measurement precision.
2Productivity
If keyword-based approach is used for content rating, then text filtering effectiveness improves, but applicability to audiovisual contents deteriorates due to lack of comprehensive description capability
Solution Approach 1:
The content rating system is designed to handle multiple content types uniformly through a single framework. It combines keyword-based text filtering with metadata-based analysis (including ATSC and TV-Anytime metadata) to create a universal rating mechanism that works effectively for both text and audiovisual contents.
Solution Approach 2:
The content rating approach uses a composite evaluation method that integrates multiple data sources: keyword matching, ATSC metadata, TV-Anytime metadata, and user profile information. This composite approach leverages the strengths of each method while compensating for their individual limitations.
3Ease of manufacture
If depth-based method is used to measure associativity between contents, then computational simplicity improves, but measurement accuracy deteriorates due to overlooking actual distance between nodes
Solution Approach 1:
The system transitions from single-dimensional depth measurement to multi-dimensional associativity measurement by incorporating path length calculations. This adds a temporal/distance dimension to the evaluation, allowing the system to measure both the depth of hierarchical relationships and the actual path distance between content nodes in the semantic network.
4Measurement precision
If path length-based method is used to measure associativity, then consideration of node depths improves, but computational complexity increases due to the need to count links between nodes
Solution Approach 1:
The system pre-computes and stores path length information between content nodes in the semantic network during an initial indexing phase. This preliminary action allows the associativity measurement to be performed quickly during content recommendation without requiring complex real-time path calculations, thus reducing operational computational complexity.
Data Source
AI summary
Provided are a method and system for recommending content. The method and system enable a user to be given recommendations of contents similar to what he/she likes. The method for recommending content includes creating a user profile according to a predetermined model based on a user's reaction to the content, obtaining content features from one or more data sources, and creating a list of recommended contents according to a predetermined process based on the user profile and the content features. The system for recommending content includes a user profiling module that creates user profiles according to a predetermined model based on a user's reaction to the content, a digital television module that obtains content metadata from one or more data sources, and a content rating module that creates a list of recommended contents based on the user profile and the content metadata received from the user profiling module and the digital television module. The user can be given recommendations of contents similar to what he/she likes.