Attribute-Based Item Bundling for Complementary Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face difficulties in identifying items or information that complement their initial search queries, such as food items, articles, or news topics, as existing systems struggle to provide relevant recommendations based on grouping information.
Innovation Solution
A method and system that identifies a first item of interest, classifies its attributes, finds similar items, and recommends bundles of items that include these similar items, ranked according to specific criteria, using hardware processors and machine learning algorithms to enhance relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users search for a specific item, then they receive information about that item, but they fail to receive recommendations for complementary items or related information
Solution Approach 1:
The patent segments the recommendation process into distinct stages: identifying the initial item, classifying its attributes, finding similar items based on those attributes, and grouping them into bundles. This segmentation allows the system to systematically provide complementary information that would otherwise be lost.
Solution Approach 2:
The patent introduces an intermediary classification system that acts as a mediator between the user's initial search and the recommendation engine. By classifying items into categories and attributes, the system bridges the gap between specific user queries and broader complementary recommendations.
2Ease of operation
If the system provides comprehensive item recommendations, then user satisfaction improves, but system complexity increases
Solution Approach 1:
The patent creates a universal classification framework that can handle multiple types of items (products, articles, information) through a single system. This multi-functional approach allows comprehensive recommendations without proportionally increasing complexity, as the same classification and bundling logic applies across different domains.
Solution Approach 2:
The system changes parameters by focusing on key attributes and categories rather than analyzing every possible item characteristic. By adjusting the granularity of classification and using predefined bundles, the system maintains simplicity while providing comprehensive recommendations.
3Measurement precision
If the system analyzes multiple item attributes for similarity, then recommendation accuracy improves, but processing time increases
Solution Approach 1:
The patent applies partial action by selecting and analyzing only the most relevant attributes for similarity measurement rather than examining all possible characteristics. This selective approach maintains high accuracy in recommendation while significantly reducing processing time compared to comprehensive attribute analysis.
Solution Approach 2:
The system performs preliminary classification of items into categories and attributes before conducting similarity analysis. This preliminary organization reduces the search space and allows for faster, more accurate comparison of relevant features without processing unnecessary information.
Data Source
AI summary
Methods, systems, and media for recommending information based on grouping information are provided. In some embodiments, the method comprises: identifying a first item of interest to a user of a user device; receiving data corresponding to the first item; classifying, using a hardware processor, the data to obtain one or more attributes of the first item; identifying a second item similar to the first item based on similarity of the one or more attributes of the first item to one or more attributes of the second item; identifying a plurality of bundles of items that each includes the second item; ranking the plurality of bundles of items; and selecting a bundle of items from the plurality of bundles of items for recommendation to the user of the user device.