Sparse Coding for Catalog Item Diversity and Relevance
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Solution Overview
Problem
Existing product recommendation systems often generate overly specific or irrelevant recommendations due to reliance on customer purchase history, failing to balance category diversity and item relevance effectively in large electronic catalogs.
Innovation Solution
A product recommendation system that uses sparse coding techniques to select categories based on similarity scores and target metrics, maximizing both category diversity and relevance by reconstructing the category popularity signal with a fixed number of positive elements, thereby selecting a diverse and relevant set of items for recommendation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If product recommendation systems rely on customer purchase history, then item relevance is improved, but category diversity deteriorates
Solution Approach 1:
The patent segments the recommendation problem into two distinct optimization objectives: item relevance (based on purchase history) and category diversity. By separating these concerns and optimizing them jointly through a mathematical framework, the system achieves both specific relevant recommendations and diverse category coverage without one undermining the other.
Solution Approach 2:
The patent transforms the recommendation problem into a parameter optimization problem where category similarity scores and target metrics are used as parameters. By changing the parameter representation from simple categorical labels to continuous similarity scores derived from mutual information, the system can mathematically optimize both relevance and diversity simultaneously.
2Adaptability or versatility
If sparse coding techniques are used to select categories, then category diversity is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential elements needed for diversity optimization by using sparse coding to select a small subset of N categories from a large catalog. This extraction approach focuses computational resources on identifying and processing only the most relevant diverse categories rather than analyzing all possible items, thereby managing complexity while maintaining diversity.
Solution Approach 2:
The patent applies partial action by selecting a fixed number N of categories that provides sufficient diversity without requiring exhaustive analysis of all categories. This partial approach to category selection achieves adequate diversity while significantly reducing computational complexity compared to comprehensive analysis.
3Productivity
If a fixed number of categories are selected using sparse coding, then scalability is improved, but measurement precision of category similarity may deteriorate
Solution Approach 1:
The patent performs preliminary computation of category similarity scores and mutual information metrics before the actual recommendation generation. By pre-calculating these similarity measures and storing them, the system enables fast scalable recommendations without sacrificing measurement precision during the actual recommendation query processing.
Data Source
AI summary
Methods, systems, and computer-readable media for optimizing diversity and relevance for catalog items are disclosed. Respective target metrics are determined for a plurality of categories comprising a plurality of items in an electronic catalog. First and second categories comprise first and second subsets of the plurality of items, and the respective target metric for the first category is determined using aggregation of individual target metrics for the first subset. Respective similarity scores are determined between pairs of the categories. N item categories are selected from the plurality of categories based at least in part on the respective target metrics and based at least in part on the respective similarity scores. A third subset of the plurality of items is selected from across the N item categories, including the first and second categories. The selected items optimize both category diversity and item relevance.