Social Network Recommender System for Popular Content Detection
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Solution Overview
Problem
Users face difficulties in finding relevant content items in large databases, and existing recommender systems rely on correlations between user preferences and content characteristics, missing opportunities to recommend highly discussed items that may not align with individual tastes.
Innovation Solution
A method and system that monitor communication messages in a social network to detect frequently mentioned content items, generating and sending recommendation messages to users about highly discussed topics, regardless of personal preferences, thereby reducing the need for users to actively search for popular content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users actively search for popular content items in large databases, then they can find relevant content, but it increases energy consumption and network load
Solution Approach 1:
The system performs preliminary actions by proactively monitoring social network communications and identifying popular content items before users search for them. The recommender system detects content item identifiers in monitored messages, counts occurrences, and generates recommendation messages in advance, so users receive popular content recommendations without needing to actively search, thereby reducing their energy consumption.
2Loss of information
If users actively search for popular content items in large databases, then they can find relevant content, but it increases network load
Solution Approach 1:
The system performs preliminary actions by proactively monitoring social network communications and identifying popular content items before users search for them. The recommender system detects content item identifiers in monitored messages, counts occurrences, and generates recommendation messages in advance, so users receive popular content recommendations without needing to actively search, thereby reducing network load.
3Adaptability or versatility
If recommender systems use collaborative filtering to analyze user history, then they can provide personalized recommendations, but they miss highly discussed items that don't align with individual preferences
Solution Approach 1:
The system merges collaborative filtering-based personalized recommendations with talk-of-the-town recommendations based on social network monitoring. By combining these two approaches, the system maintains adaptability to individual user preferences while also informing users about highly discussed content items that may not align with their personal tastes, thus preventing loss of information about popular content.
4Loss of information
If the system monitors all communication messages to detect popular content, then it can identify highly discussed topics, but it increases system complexity
Solution Approach 1:
The system extracts only the necessary information from communication messages - specifically content item identifiers - rather than analyzing the complete message content. This extraction approach allows the system to detect popular content by counting identifier occurrences while minimizing processing requirements and system complexity.
Data Source
AI summary
The present invention relates to a method of operating a recommender system arranged for being coupled to a computer implemented social network (200). The present invention furthermore relates to a corresponding computer program and to a corresponding recommender system (100) arranged for being coupled to a computer implemented social network (200). In particular, the present invention relates to a recommender system (100) being configured to provide a recommendation (172) relating to a content item being highly discussed in the social network (200), without the recommendation being necessarily based on some kind of a correlation between characteristics of a recipient (10) of the recommendation on the one side and characteristics of content items to be recommended and/or characteristics of contacts/friends of the recipient (10) on the other side.