Group Media Recommendation via Partial Viewing Duration Analysis
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Solution Overview
Problem
Traditional media recommendation systems fail to effectively suggest media assets that cater to the diverse viewing preferences of a group of people, as they lack the ability to target individual users within a group based on their unique viewing histories, leading to inappropriate recommendations.
Innovation Solution
A media guidance application identifies groups of users and selects media assets that each user has partially accessed, determining the duration each user has viewed and comparing these durations to provide recommendations. It considers the overlap and non-overlap periods, critical portions, and user interests to recommend media assets that are suitable for the group, with options to manually or automatically generate recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional media recommendation systems are used, then the system is simple to operate, but the recommendations are not personalized for individual users within a group
Solution Approach 1:
The patent segments the group into individual user profiles, each with their own viewing history and preferences. The system analyzes each user's consumption patterns separately and generates personalized recommendations within the group context, allowing individualization without requiring separate systems for each user.
Solution Approach 2:
The patent adds a new dimension of analysis by tracking not just what media users watch, but how much of each media asset they consume (partial vs. complete viewing). This temporal dimension of consumption allows the system to differentiate between users at different stages of media consumption and provide context-aware recommendations.
2Productivity
If manual browsing of media assets is required, then the system does not require complex algorithms, but the user must manually browse through lists of assets which is time-consuming
Solution Approach 1:
The system performs preliminary analysis of each user's viewing history and consumption patterns in advance, building profiles that capture individual preferences and viewing behaviors. This pre-computed information enables rapid generation of personalized recommendations without requiring users to manually browse through media lists.
3Adaptability or versatility
If the system recommends media assets that one user has watched but others have not, then individual preferences are respected, but the group experience is compromised
Solution Approach 1:
The patent changes the parameters used for recommendation by incorporating the degree of media consumption (partial vs. complete viewing) as a key factor. Instead of simply noting whether a user has watched a media asset, the system analyzes how much of each asset has been consumed, allowing it to distinguish between users who have seen different portions of the same content and make group-compatible recommendations accordingly.
Data Source
AI summary
Systems and methods are described herein for providing media recommendations for a group. A media guidance application may identify a group comprising at least a first user and a second user and select a media asset of which the first and second users have previously accessed less than a duration of the media asset. For example, each of the first and the second users may have watched only a respective portion of the media asset. The media guidance application may determine a first amount of the media asset duration that the first user has accessed the media asset and a second amount of the media asset duration that the second user has accessed the media asset. Based on the first and second amounts of the media asset duration, the media guidance application may provide a recommendation for the media asset to the group.