Fine-Grained Recommendation Systems via Content Segmentation
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Solution Overview
Problem
Modern recommendation systems fail to provide fine-grained, user-specific recommendations by not considering the actual sub-parts of content objects and their locations, leading to irrelevant suggestions.
Innovation Solution
The implementation of techniques that analyze and utilize user interactions with content management systems to generate recommendations based on the specific sub-parts and locations of content objects, such as chapters, scenes, or sections, within content management systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendation systems analyze user interactions with content objects, then user-specific recommendations can be generated, but the systems fail to consider sub-parts and locations of content objects leading to irrelevant suggestions
Solution Approach 1:
The patent segments content objects into distinct sub-parts (chapters, scenes, sections, timestamps) and tracks user interactions with each segment separately. This allows the recommendation system to identify which specific portions of content users engage with, enabling precise recommendations of similar sub-parts rather than generic whole-content recommendations.
Solution Approach 2:
The patent adds a temporal and spatial dimension to content analysis by tracking not just which content objects users interact with, but also the specific locations (timestamps, chapter positions, scene identifiers) within those objects. This dimensional enrichment transforms coarse-grained content metadata into fine-grained interaction data, enabling location-aware recommendations.
2Adaptability or versatility
If recommendation systems provide coarse-grained content suggestions, then implementation is simpler, but the recommendations are less relevant to user specific interests
Solution Approach 1:
The system segments both the content objects and user interactions into fine-grained components. By dividing content into sub-parts (scenes, chapters, timestamps) and tracking interactions at this granular level, the system achieves high adaptability to user specific interests while managing complexity through structured data organization and processing.
Solution Approach 2:
The patent applies local quality by making recommendations specific to particular portions of content objects rather than treating all content uniformly. The system analyzes user interactions with specific sub-parts and generates recommendations tailored to those local preferences, thereby increasing recommendation relevance without requiring complete system redesign.
3Measurement precision
If recommendation systems track detailed user interactions with content sub-parts, then fine-grained recommendations can be generated, but data processing complexity increases
Solution Approach 1:
The patent structures interaction data by segmenting it into discrete, standardized units associated with specific content sub-parts. This segmentation enables efficient storage, retrieval, and processing of fine-grained interaction data, as the system can query and analyze specific segments without processing entire content objects, thereby managing data processing complexity.
Solution Approach 2:
The system creates structured copies of interaction data that capture essential information (user ID, content ID, sub-part location, interaction type) without storing complete raw interaction logs. This data copying and abstraction approach maintains measurement precision while reducing processing complexity by working with condensed, standardized data representations.
Data Source
AI summary
A recommendation system integrated with a content management system (CMS). The CMS stores instances of shared content objects and coordinates user interactions by and between a plurality of CMS users. Shared content objects are divided into a plurality of portions, after which any user interactions over the various portions of the content object are observed and analyzed. User interest inferences are drawn from analysis of the observed user interactions taken user over respective particular portions of the content object. Based on the inferred user interests, fine-grained recommendations are formed and propagated. Some fine-grained recommendations refer to further content objects (e.g., content objects of different types). Some fine-grained recommendations are propagated to other CMS users (e.g., to a plurality of CMS users that are related in some way). The fine-grained recommendations refer to one or more specific portions of a content object as well as to the content object itself.