Personalized Search Using Profile Segmentation
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Solution Overview
Problem
Conventional web searches lack sensitivity to factors external to the search query, such as searcher characteristics and content relevance, leading to less pertinent results.
Innovation Solution
A search engine system that utilizes member profile data, user interests, and behavioral factors to generate personalized search results by analyzing past activities and similarities with other users, combining these with conventional search factors like keywords and link density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search factors (keywords, link density, sponsorship) are used to rank search results, then the search system is simple and fast, but the search results lack sensitivity to searcher characteristics and content relevance
Solution Approach 1:
The patent segments the search system into multiple independent modules: a conventional search module that processes keywords and link density, a searcher profile module that analyzes user characteristics, and a content analysis module that evaluates content relevance. Each module operates independently and contributes to the final ranking, allowing the system to maintain simplicity in individual components while achieving complex personalized search results through their integration.
2Measurement precision
If personalized search factors (searcher profile, user interests, behavioral factors) are incorporated, then search result pertinency improves, but computational requirements and processing time increase
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing searcher profiles, user interests, and behavioral factors in databases before actual search queries are processed. When a search is executed, the system quickly retrieves pre-analyzed profile data and matches it with the query results, rather than performing complex analysis in real-time. This significantly reduces processing time while maintaining high search result pertinency.
3Measurement precision
If multiple search factors are combined for personalized results, then search quality improves, but the system requires more data processing and storage resources
Solution Approach 1:
The patent extracts only the most relevant features from searcher profiles and content data for each specific search query, rather than processing all available data. The system identifies and extracts key behavioral factors, interest categories, and content attributes that are directly relevant to the current search, discarding or de-emphasizing irrelevant information. This selective extraction maintains high search quality while reducing the quantity of data that needs to be processed and stored.
Data Source
AI summary
A system and method for personalized search user searcher features may include obtaining a search term from a member of a social network at a user device via the network interface. An initial result may be generated based on the search term, including a first group of content items from a social network and stored in a content database, the content items including member profiles of members of the social network. Each of the content items of the first group may be ranked based on information indicative of interactions from an activity database with the content items of the first group, the interactions being by at least a second user of the social network different than the first user. A second group of the content items may be displayed, including at least some of the first group of the content items, based on the rank of the first group of the content items.