Recommendation Spacing via Repulsion Force Analysis
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Solution Overview
Problem
Existing recommendation algorithms in online environments often fail to provide diverse and interesting recommendations, leading to user dissatisfaction and reduced sales revenue, as they tend to cluster around a single theme or obvious items, which can be monotonous and uninformative.
Innovation Solution
The implementation of 'repulsion force' analysis to generate spaced recommendations, which separates original recommendations from each other and from known user interests, ensuring a diverse and novel set of items is presented, thereby reducing clustering and emphasizing less obvious choices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendation algorithms generate recommendations based on user purchase history and interest assessment, then the recommendations match user interests and increase relevance, but the recommendations cluster around a single theme and become monotonous
Solution Approach 1:
The patent segments the recommendation generation process into multiple independent modules: an interest assessment module that identifies user preferences, and a recommendation generation module that creates diverse recommendations across multiple themes. This segmentation allows the system to maintain high relevance within each theme while providing variety across themes, resolving the contradiction between relevance and diversity.
Solution Approach 2:
The patent merges multiple recommendation themes and sources into a unified recommendation set. By combining recommendations from different thematic categories and sources, the system achieves both relevance (through interest-matched themes) and diversity (through multi-theme composition), directly addressing the technical contradiction.
2Productivity
If recommendation algorithms focus on highly related items to maximize relevance, then sales conversion increases, but user engagement decreases due to monotonous content
Solution Approach 1:
The patent applies local quality by making different parts of the recommendation list serve different functions: highly relevant recommendations (local clusters) drive immediate conversions, while diverse recommendations from multiple themes maintain overall user engagement. Each local segment has optimized quality for its specific purpose, resolving the contradiction between conversion and engagement.
Solution Approach 2:
The patent introduces dynamics by allowing the recommendation system to adaptively adjust the balance between highly relevant items and diverse items based on user interaction patterns. The system dynamically modifies recommendation composition to maintain both high conversion potential and sustained user engagement, preventing monotony while preserving sales effectiveness.
3Measurement precision
If recommendation algorithms provide obvious and highly related recommendations, then relevance is maximized, but novelty and user interest are reduced
Solution Approach 1:
The patent adds another dimension to the recommendation space by introducing multiple thematic categories beyond the primary user interest. Instead of only recommending items within the most obvious interest category, the system expands into additional thematic dimensions, providing both accurate interest-matched recommendations and novel discoveries across different dimensions.
Solution Approach 2:
The patent uses thematic categories as intermediaries between user interests and recommended items. These intermediary themes allow the system to bridge obvious user preferences with less obvious but still relevant recommendations, maintaining accuracy while introducing novelty through the intermediary thematic layer.
Data Source
AI summary
Strategies are described for generating recommendations. The strategies generate a set of original recommendations based on a source of information. The strategies then transform the set of original recommendations into a set of similarity-spaced recommendations based on “repulsion force” analysis applied to the set of original recommendations. In a first implementation, the set of spaced recommendations represent a diverse sampling of items in the set of original recommendations. In a second implementation, the set of spaced recommendations represent a sampling of items in the set of original recommendations which omits or excludes recommendations assessed as obvious. A third implementation can combine the first and second implementations.