Social Network Media Recommendation Engine
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Solution Overview
Problem
Current media recommendation systems rely solely on individual user usage patterns, struggle to characterize complex and evolving user tastes, and are limited by pre-analyzed central databases, failing to provide comprehensive recommendations due to their restricted scope.
Innovation Solution
A Social Network Group-Based Media Content Recommendation Engine that utilizes media usage patterns of an entire social group, combining statistical and analytical analysis to generate recommendations, incorporating both user consumption data and media content parameters, and employing DSP techniques to match user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a pre-analyzed central database of media is used, then the system structure is simple and centralized, but the scope and diversity of recommendations are limited
Solution Approach 1:
The patent segments the centralized database into multiple distributed user databases, where each user has their own database containing media they have accessed. This segmentation allows the system to maintain simplicity in structure while dramatically expanding the scope of available media for recommendations by leveraging the collective libraries of multiple users.
Solution Approach 2:
The patent implements a nested structure where individual user databases are contained within a broader social network context. Each user database is nested within the larger network of connected users, allowing the system to provide recommendations that are personalized to the individual while drawing from the collective media libraries of the entire social network group.
2Ease of operation
If only individual user usage patterns are analyzed, then the system is simple to operate, but it fails to capture complex and evolving user tastes
Solution Approach 1:
The patent merges individual usage pattern analysis with social network connection data. By combining these two data sources, the system maintains operational simplicity while significantly improving its ability to characterize user tastes, as it now considers both what the user likes and what their social connections like.
Solution Approach 2:
The patent implements feedback loops where the system continuously learns from user interactions with recommendations and adjusts future recommendations accordingly. This feedback mechanism allows the system to adapt to evolving user tastes over time, improving measurement precision without increasing operational complexity.
3Manufacturing precision
If DSP techniques are applied to analyze usage history, then media properties can be extracted, but it becomes difficult to characterize human media tastes accurately
Solution Approach 1:
The patent introduces social network connection data as an intermediary between DSP technique analysis and final taste characterization. Instead of relying solely on DSP-extracted media properties, the system uses social connections as a mediator to bridge the gap between objective media properties and subjective user tastes, improving characterization accuracy.
Solution Approach 2:
The patent changes the parameters used for taste characterization from purely DSP-based media properties to a combination of usage patterns, social network connections, and contextual information. This parameter change allows the system to move beyond the limitations of DSP techniques in capturing human taste complexity.
4Ease of manufacture
If a limited central database is used, then database maintenance is simple, but comprehensive recommendations cannot be provided
Solution Approach 1:
The patent implements a self-service model where each user maintains their own database of media they have accessed or own. This eliminates the burden of maintaining a comprehensive centralized database, as the system leverages the collective libraries of all users. Each user's database is maintained by that user through their normal media consumption activities.
Solution Approach 2:
The patent makes each user database serve multiple functions: it stores the user's personal media library, provides the basis for personalized recommendations, and contributes to the collective knowledge base of the social network. This multi-functionality allows the system to provide comprehensive recommendations without requiring a separately maintained centralized database.
Data Source
AI summary
The invention relates to a method and device for selecting at least one media for recommendation to a user. The device comprises an interface to a users database comprising data relative to users medias consumption and data relative to connections between users. The device further comprises a statistical analyzer receiving data relative to users medias consumption and connections between users in inputs and generating a first list of at least one media for output. The device comprises an interface to a medias library comprising parameters indicative of medias content. The device also comprises an analytical analyzer receiving data relative to users medias consumption and parameters indicative of medias content in inputs and generating a second list of at least one media for output. The device also comprises a recommendation engine receiving the first and second lists of media and selecting at least one media for recommendation to the user.