Play History Based Digital Work Recommendation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital player devices lack an intelligent mechanism to recommend digital works based on user play histories, often relying on user profiles or purchase data, which can lead to unreliable recommendations if profiles are not detailed or if works are downloaded from various sources.
Innovation Solution
A system that collects metadata from user devices about played digital works, analyzes play histories to detect relationships between works and creators, and uses this data to provide personalized recommendations by identifying co-occurrence frequencies and user-generated playlists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user profiles or purchase data are used for recommendations, then recommendations can be provided, but reliability deteriorates when profiles are not detailed or works are downloaded from multiple sources
Solution Approach 1:
The patent introduces a metadata intermediary layer that translates diverse digital works from multiple sources into a standardized format. This metadata mediator enables reliable recommendations by creating a common language for comparing works across different sources, without requiring detailed user profiles or centralized purchase data.
Solution Approach 2:
The system creates copies of work characteristics through metadata extraction. Instead of relying on original user profiles or purchase records, the system generates metadata copies that capture essential features of digital works, enabling recommendation without needing the original detailed user data.
2Reliability
If detailed user profiles are required for accurate recommendations, then recommendation accuracy improves, but device complexity and user burden increase
Solution Approach 1:
The system implements self-service by automatically extracting metadata from digital works during normal playback. Users don't need to manually create or maintain profiles; the system serves itself by gathering necessary information from the works themselves through automated metadata extraction and analysis.
Solution Approach 2:
The system performs preliminary analysis of digital works by extracting and storing metadata in advance. This preliminary action prepares recommendation data beforehand, so when recommendations are needed, the system can quickly generate accurate suggestions without requiring users to have pre-created detailed profiles.
3Adaptability or versatility
If metadata is collected from multiple sources, then recommendation coverage improves, but data processing complexity increases
Solution Approach 1:
The patent creates a universal metadata framework that can handle digital works from multiple sources. This universal metadata structure serves multiple functions: it standardizes diverse formats, enables cross-source comparison, and works with different types of digital works, thereby achieving multi-source compatibility without proportionally increasing processing complexity.
Data Source
AI summary
A computer-implemented service recommends digital works (and/or creators of works) to a user based on works currently or previously played or downloaded by the user on a player device or based on playlists stored on the player device. The works may be, for example, music files, video files, electronic books, or other digital content for playing by users. A user may thus obtain personalized recommendations that are based on works obtained from sources (web sites, physical CDs, etc.) that are independent of the recommendations system. In one embodiment, the service identifies pairs of works (and/or work creators) that are similar to each other by virtue of the relatively high frequency with which they co-occur on playlists or within play histories of users. The resulting mappings are used to provide recommendations to users.