Search Result Ranking Using User Behavior and Entity Attributes
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Solution Overview
Problem
Current search engines often present users with a large number of irrelevant results due to the vast amount of data being searched, making it difficult to identify relevant information, as they fail to adequately consider item characteristics and user behavior in ranking search results.
Innovation Solution
A method and system that arranges search results by considering not only the content match but also the characteristics of the data entity, such as age, and previous user interactions, such as retrieval or modification, to enhance the relevance of search results displayed to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search engines present a large number of results to users, then the quantity of information provided increases, but the relevance and quality of results decreases
Solution Approach 1:
The patent changes the parameters used for ranking search results from traditional keyword-matching metrics to include item characteristics (such as age, type, and metadata) and user behavior patterns. This allows the system to maintain a large quantity of results while improving their relevance through multi-dimensional evaluation parameters.
Solution Approach 2:
The system incorporates user behavior feedback (such as click-through rates, time spent on results, and interaction patterns) into the ranking algorithm. This feedback loop continuously refines the relevance of search results by learning from actual user interactions, ensuring that presented results are both numerous and relevant.
2Device complexity
If search engines rely solely on keyword matching, then the simplicity of the search algorithm is maintained, but the accuracy of identifying relevant information decreases
Solution Approach 1:
The patent segments the search ranking process into multiple independent components: keyword matching, item characteristic evaluation, and user behavior analysis. Each component operates with its own set of criteria and can be independently optimized, allowing the system to achieve high accuracy without overwhelming complexity in any single area.
3Productivity
If search engines do not consider user behavior, then the speed of result generation is maintained, but the personalization and relevance of results to individual users decreases
Solution Approach 1:
The system performs preliminary analysis and categorization of user behavior patterns in advance, creating user profiles and preference models before search queries are executed. This pre-processing allows the search engine to quickly retrieve and rank results according to established user preferences without adding significant delay to the actual search operation.
Data Source
AI summary
A method and system for information retrieval are provided whereby at least one search criterion is received from a user; a query is created based on the at least one search criterion; the query is executed to generate results, each of the results corresponding to a respective data entity which satisfies the at least one search criterion; the results are arranged into an order, the order being determined at least in part by a characteristic of the data entity corresponding to each result and a previous act by a user with respect to the data entity corresponding to each result; and the results are displayed to the user.