User Recommender System Using Concurrency Metrics
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Solution Overview
Problem
Existing recommender systems fail to effectively recommend users to each other within a community based on their interaction patterns and preferences, lacking a robust method to quantify user similarity and relevance.
Innovation Solution
A user recommender system that utilizes user profiles and item profiles, leveraging metrics such as co-concurrency, pre-concurrency, and post-concurrency to determine user similarity, and employs a knowledge base of mediasets to identify relevant users by intersecting their interaction data, thereby recommending users based on affinity, discovery, and guidance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing recommender systems are used, then item recommendations can be provided, but user-to-user recommendations cannot be effectively generated
Solution Approach 1:
The patent transforms the recommendation problem from item-based to user-based by changing the fundamental parameter being recommended. It introduces new metrics (co-concurrency, pre-concurrency, post-concurrency) that quantify user interaction patterns, enabling the system to recommend users rather than just items while maintaining reliability through mathematically rigorous similarity measurements.
Solution Approach 2:
The patent introduces an intermediary layer of metrics and measurements that mediate between user interactions and recommendations. By defining co-concurrency, pre-concurrency, and post-concurrency as intermediate computational steps, the system bridges the gap between raw interaction data and meaningful user recommendations, solving both the versatility and reliability challenges.
2Adaptability or versatility
If user similarity metrics are introduced, then user recommendations can be generated, but system complexity increases
Solution Approach 1:
The patent segments the user similarity measurement into three distinct computational components: co-concurrency (simultaneous interactions), pre-concurrency (sequential interactions), and post-concurrency (reverse sequential interactions). This segmentation makes the complex task of user similarity measurement more manageable and computationally efficient, reducing overall system complexity while maintaining versatility.
Solution Approach 2:
The patent changes the parameters of user interaction from simple binary flags to multi-dimensional metrics. By introducing co-concurrency, pre-concurrency, and post-concurrency parameters, the system captures nuanced user relationship dynamics without requiring complex architectural changes, thus achieving versatility with controlled complexity.
3Measurement precision
If comprehensive interaction data is analyzed, then recommendation accuracy improves, but computational requirements increase
Solution Approach 1:
The patent extracts only the most relevant features from comprehensive interaction data by focusing on three specific concurrency metrics. Instead of analyzing all possible interaction attributes, it selectively extracts co-concurrency, pre-concurrency, and post-concurrency measurements, achieving high measurement precision while reducing computational resource requirements by eliminating unnecessary data processing.
Solution Approach 2:
The patent applies partial action by computing only the specific concurrency metrics needed for user recommendation rather than performing exhaustive analysis of all interaction data. This selective computation approach maintains measurement precision for the critical dimensions of user similarity while avoiding the computational overhead of complete data analysis.
Data Source
AI summary
Disclosed are embodiments of systems and methods for recommending relevant users to other users in a user community. In one implementation of such a method, two different sets of data are considered: a) music (or other items) that users have been listening to (or otherwise engaging), and b) music (or other items) recommendations that users have been given. In some embodiments, pre-computation methods allow the system to efficiently compare item sets and recommended item sets among the users in the community. Such comparisons may also comprise metrics that the system can use to figure out which users should be recommended for a given target user.