Media Recommendation System Indecision Detection
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Solution Overview
Problem
Users are overwhelmed by the vast number of media content choices and lack a practical way to obtain credible recommendations for media assets they are undecided about, often relying on friends and family, but not efficiently accessing recommendations from others due to the sheer volume of options.
Innovation Solution
A system that detects indecision patterns in user interactions with media assets, such as repeated requests for ratings information or short viewing durations, and sends electronic queries to users' social circles who have viewed the asset, aggregating and weighting responses for timely and credible recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If users rely on traditional recommendation systems based on consumption profiles, then recommendations are provided automatically, but the credibility of recommendations is reduced
Solution Approach 1:
The system introduces an intermediary mechanism that bridges automated systems and human judgment. It automatically identifies users experiencing indecision and facilitates communication between them and their social connections, allowing human credibility to enhance automated recommendations without requiring full manual intervention
Solution Approach 2:
The system implements feedback loops where recommendation requests are routed to appropriate social connections based on user profiles and relationship data. The responses from these connections provide credible human validation that feeds back into the recommendation process, improving overall recommendation credibility while maintaining automation
2Reliability
If users collect recommendations from other people for every available media asset, then recommendation credibility increases, but the time and effort required becomes impractical
Solution Approach 1:
The system performs preliminary actions by proactively detecting indecision patterns through monitoring user interactions with media assets. When indecision is detected, the system automatically initiates the recommendation collection process by identifying appropriate social connections and routing requests, eliminating the need for users to manually search for and contact multiple people
Solution Approach 2:
The system enables a form of self-service where the automated infrastructure handles the time-consuming tasks of identifying indecision, selecting appropriate recommenders from social networks, and aggregating responses. Users benefit from credible human recommendations without investing the practical time and effort previously required
3Loss of information
If the system queries all people in a user's social circle for every media asset, then comprehensive recommendations are obtained, but the burden on friends and family becomes excessive
Solution Approach 1:
The system applies local quality by tailoring the recommendation query distribution to match the specific characteristics of each user's indecision pattern and the particular media asset. It selectively routes queries to specific social connections based on their demonstrated expertise, viewing history, and relevance to the asset in question, rather than uniformly burdening the entire social circle
Data Source
AI summary
Methods and systems for providing media asset recommendations are described. An input to a user interface requesting content associated with a media asset is detected without corresponding display of the media asset on a display device associated with the user interface. In response, a user interaction history comprising interaction data associated with the media asset is retrieved. The interaction data is analyzed to identify an indecision pattern. In response to detecting the indecision pattern, a plurality of people are identified based on a match between the media asset and media profile information of the people. An electronic query is transmitted to each of the people requesting a recommendation for the media asset. The received responses are generated for display for the user.