Search Result Ranking via Interest Trend Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Search engines often return a large number of irrelevant results due to the vast amount of data available, making it difficult for users to find items that align with their specific interests, even when those interests are trending among other users.
Innovation Solution
The technology uses ontologies and trending interest analysis to suggest relevant products and categories based on user search queries, comparing them with recent searches from other users and dynamically updating to reflect changing interests, thereby providing more targeted search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search engines return a large number of results to cover vast data, then the quantity of search results is improved, but the relevance to user interests deteriorates
Solution Approach 1:
The patent changes the parameter of result selection from pure keyword matching to a composite scoring system that incorporates user interest profiles, trending analysis, and relevance weights. This transforms the search result generation process to balance quantity and relevance through dynamic parameter adjustment based on user preferences and current trends.
Solution Approach 2:
The system implements feedback loops where user search behavior, click-through patterns, and interest expressions are continuously analyzed to refine future search results. Trending interest analysis provides feedback on what topics are currently popular, allowing the system to adjust result relevance dynamically based on evolving user interests rather than static keyword matching.
2Reliability
If search engines display highly relevant results on top, then the quality of top results is improved, but the diversity of interesting items deteriorates
Solution Approach 1:
The patent applies local quality by differentiating the ranking criteria for different positions in the search results. Top results are optimized for high relevance and quality, while lower positions incorporate trending items and diverse interesting content. This allows each section of the results to serve a different purpose, maintaining overall system effectiveness while providing both quality and diversity.
Solution Approach 2:
The system dynamically adjusts the composition of search results based on real-time trending analysis and user interest patterns. The diversity of interesting items is not fixed but adapts according to current trends, allowing the search engine to provide both high-quality relevant results and diverse trending content that reflects evolving user interests.
3Speed
If search engines use keyword matching to find results, then the speed of search is improved, but the precision of interest alignment deteriorates
Solution Approach 1:
The system performs preliminary action by pre-processing and indexing user interest profiles, trending topics, and content metadata before actual search execution. This pre-computation allows the search engine to quickly match user queries against pre-analyzed data structures, maintaining high speed while enabling sophisticated interest alignment through pre-established relationships and weighted associations.
Data Source
AI summary
Technology is described for providing search results based on a search query. The method may include receiving the search query. A user interest based on the search query may also be identified. The user interest may be compared with currently trending interests. Interest items based on the currently trending interests that relate to the user interest may be identified.