Intent-Driven Search Ranking via Collaborative Filtering
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Solution Overview
Problem
Existing search engines fail to accurately rank and filter search results based on user intent, as they rely on relevance and pre-selected domains, which may not reflect the user's actual interests, especially when user-specific information is scarce.
Innovation Solution
Implementing an intent-driven search system that estimates user intent through collaborative filtering, using search context and user behavior data to enhance search result ranking and filtering, allowing for better differentiation of search results based on intended actions such as saving or sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search engines rank and filter results based on relevance and pre-selected domains, then search results are provided systematically, but the results may not reflect the user's actual interests when user-specific information is scarce
Solution Approach 1:
The patent introduces collaborative filtering as an intermediary mechanism that mediates between limited user-specific information and the need for accurate intent prediction. By incorporating other users' behavior data as an intermediary source, the system compensates for the lack of individual user information while maintaining ranking accuracy.
Solution Approach 2:
The system applies multi-functionality by using collaborative filtering to serve multiple purposes: predicting user intent, ranking search results, and compensating for scarce user data. This universal approach allows the same mechanism to address various aspects of the information retrieval problem simultaneously.
2Measurement precision
If collaborative filtering is used to estimate user intent, then search result accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the search system into distinct functional modules: a search engine component for retrieving results and a collaborative filtering component for estimating user intent. This segmentation allows each module to specialize in its function while reducing overall system complexity through modular design.
Solution Approach 2:
The collaborative filtering module acts as an intermediary between the search engine and the user, translating raw user behavior data into intent estimates that can be used for ranking. This intermediary layer simplifies the overall architecture by handling the complexity of intent prediction in a dedicated component.
Data Source
AI summary
The present teaching relates to searching. In one example, a search query is received from a person. A plurality of search results are retrieved based on the search query. An intent of the person is estimated with respect to at least some of the plurality of search results. The estimated intent is what the person intends to do with respect to the at least some of the plurality of search results. The plurality of search results are provided based on the estimated intent of the person.