Multi-Perspective Learned Descriptors for Content Recommendations
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Solution Overview
Problem
Existing recommendation systems often fail to effectively suggest high-quality content items that are not popular, particularly those in the 'long tail' of content inventories, as they tend to concentrate attention on best-sellers and may miss opportunities for lesser-known but valuable content due to inadequate classification and lack of convincing justifications for recommendations.
Innovation Solution
A machine learning-based recommendation system that utilizes induced descriptors associated with multiple content description perspectives, such as single-character and multi-character perspectives, to generate interpretable recommendations by reconstructing text sequences and identifying similarities between content items, allowing for the recommendation of lesser-known items and providing explanations for the suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If best-seller information or overall popularity information is used for recommendations, then consumers are directed towards a small subset of popular items, but high-quality but less-publicized items are left with lower sales
Solution Approach 1:
The patent segments content items into different categories based on multiple perspectives (genre, author, themes, characters, settings) rather than relying solely on popularity metrics. This segmentation allows the system to identify and recommend high-quality items across diverse categories, including lesser-known content that may not be popular overall but has strong characteristics in specific segments.
Solution Approach 2:
The patent introduces multiple dimensions for content representation beyond simple popularity scores. By creating content descriptors from multiple perspectives (single-character, multi-character, event, location, temporal), the system adds dimensional complexity that enables discovery of high-quality items in the long tail of content inventories that would otherwise be overlooked.
2Ease of operation
If content consumers are classified into groups based on items borrowed or purchased, then recommendations can be tailored to group preferences, but new item content generation rate is so great that high-quality content failing to reach popularity threshold is missed
Solution Approach 1:
The patent performs preliminary analysis of content characteristics by extracting and analyzing descriptors from multiple perspectives before recommendations are generated. This preliminary action allows the system to understand the intrinsic qualities of new content items and match them against user preferences and content characteristics, enabling timely recommendations for newly released content before it achieves widespread popularity.
Solution Approach 2:
The patent changes the parameters used for content evaluation from purely popularity-based metrics to multi-perspective quality metrics. By analyzing content through multiple lenses (character development, plot structure, thematic depth, setting complexity), the system can identify high-quality new content that may not yet have achieved high sales or popularity figures.
3Ease of operation
If consumer classification approaches are used for recommendations, then group-based recommendations can be provided, but convincing or easy-to-understand justifications for recommendations cannot be provided
Solution Approach 1:
The patent introduces content descriptors as intermediary elements that bridge user preferences and content items. These descriptors serve as explanatory mediators that can be presented to users to justify recommendations. By analyzing and presenting the specific characteristics (from multiple perspectives) that make content items similar to what users have enjoyed before, the system provides transparent, easy-to-understand justifications for its recommendations.
Data Source
AI summary
At a network-accessible artificial intelligence service for generating content-based recommendations based on multi-perspective learned descriptors, text sections associated with a plurality of description perspectives, including a single-character perspective and a multi-character perspective, are extracted from various text sources. Using the text sections as input, a machine learning model which includes respective portions corresponding to the different perspectives is trained to reconstruct the input using intermediary descriptors learned from the input. An indication that a second text source is recommended with respect to a first text source is generated using a set of the learned descriptors and transmitted.