Bundle Recommendation Ranking Using Similar Item Attributes
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Solution Overview
Problem
Existing systems struggle to effectively recommend complementary items or information based on user interests, making it difficult to identify items or information that may be useful to the user.
Innovation Solution
A method and system that identifies a first item of interest, classifies its attributes, finds similar items, and recommends bundles of items or information based on similarity, using machine learning and natural language processing to enhance recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If systems recommend items based on simple user interest matching, then recommendation speed is fast, but recommendation accuracy and usefulness are poor
Solution Approach 1:
The patent segments the recommendation process into distinct stages: identifying user interests from search queries, finding similar items based on those interests, retrieving bundles containing similar items, and ranking bundles by relevance. This segmentation allows each stage to be optimized independently, improving overall recommendation accuracy without overwhelming system complexity.
Solution Approach 2:
The system performs preliminary actions by pre-identifying user interests from search queries and pre-ranking bundles based on relevance criteria before presenting recommendations. This preliminary processing improves recommendation accuracy by ensuring that only highly relevant bundles are considered, while the ranking framework is established in advance to manage complexity.
2Reliability
If systems provide detailed bundle recommendations with multiple attributes, then recommendation quality improves, but information processing time increases
Solution Approach 1:
The patent changes parameters by ranking bundles based on multiple attributes (relevance, popularity, recency) rather than relying on a single criterion. This multi-parameter approach improves recommendation quality by considering various factors, while the systematic parameter weighting and ranking framework efficiently processes these multiple dimensions without excessive time loss.
3Adaptability or versatility
If systems analyze multiple item attributes and bundle compositions, then recommendation relevance improves, but computational complexity increases
Solution Approach 1:
The patent segments attribute analysis into distinct categories: user interest attributes from search queries, item attributes for similarity comparison, and bundle attributes for relevance ranking. This segmentation allows the system to handle multiple attributes systematically, improving recommendation relevance while managing computational complexity through structured processing of each attribute type.
Solution Approach 2:
The system performs preliminary attribute extraction and similarity calculation before bundle ranking. By pre-identifying user interests and pre-calculating item similarities based on attributes, the system reduces the computational burden during the final recommendation phase, maintaining high relevance while controlling overall complexity.
Data Source
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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.