Context-Aware Recommendation System Using Interest Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional recommendation systems fail to distinguish between abstract and social interests of users, leading to content recommendations that may be inappropriate from a social perspective or context-dependent, resulting in content being recommended at one time but not another.
Innovation Solution
A recommendation pipeline that includes a crawling module to collect user data, an interest detection module to distinguish between abstract and social interests, a recommendation module to generate context-based recommendations, and a visualization module to present personalized content based on active personas, adapting content visibility and interests according to user context and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation systems recommend content based on user preferences and past activity, then content relevance from user perspective is improved, but social appropriateness deteriorates
Solution Approach 1:
The patent segments user interests into two distinct categories: abstract interests (personal preferences for content types, genres, and topics) and social interests (preferences related to social groups and contexts). This segmentation allows the recommendation system to separately analyze and weigh these different dimensions, preventing content that satisfies abstract interests but violates social appropriateness from being recommended. The system evaluates both dimensions independently before making recommendations.
2Object-affected harmful factors
If conventional recommendation systems recommend content based on social relevance, then social appropriateness is improved, but content type suitability deteriorates
Solution Approach 1:
The system segments social interests from abstract interests, allowing it to evaluate content against both dimensions. When evaluating a content item, the system checks both the abstract interest match (content type suitability) and the social interest match (social appropriateness). Only content that satisfies both segmented criteria is recommended, preventing content that is socially relevant but type-inappropriate from being suggested.
3Device complexity
If conventional recommendation systems provide static content recommendations, then system simplicity is maintained, but contextual adaptability deteriorates
Solution Approach 1:
The patent introduces dynamic contextual awareness into the recommendation system by detecting the user's current context (such as location, time, and social surroundings) and adapting recommendations accordingly. The system transitions from static, pre-determined recommendations to dynamic, context-sensitive recommendations. This allows the same user to receive different recommendations at different times based on their current social context, enhancing adaptability while maintaining a relatively simple system architecture through automated context detection.
Data Source
AI summary
Systems and methods may provide for conducting an interest analysis of data associated with a user, wherein the interest analysis distinguishes between abstract interests and social interests. Additionally, one or more recommendations may be generated for the user based on the interest analysis and a current context of the user, wherein the one or more recommendations may be presented to the user. In one example, the abstract interests identify types of topics and types of objects, and the social interests identify types of social groups.