Personalized Search Results via Probability Model
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Solution Overview
Problem
Current search engines struggle to personalize search results effectively, as they primarily rely on matching search queries with documents without considering user behavior and preferences, leading to irrelevant or less relevant results, especially with the vast and diverse information available on the web.
Innovation Solution
A method that involves searching both a corpus of documents and specialized information sources based on a search query, tracking user selection parameters, and constructing a probability model to estimate the likelihood of user preference for certain types of results, thereby selecting and scoring search results to provide more personalized outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines rely on basic query matching to determine result relevance, then the search process is simple and fast, but the personalization and relevance of results deteriorate
Solution Approach 1:
The patent segments the search result selection process into multiple independent components: a probability model that estimates user preferences, a scoring mechanism that evaluates results, and a selection module that ranks results. This segmentation allows the system to incorporate complex personalization features while maintaining modularity and manageability.
Solution Approach 2:
The system performs preliminary actions by pre-computing a probability model based on tracked user parameters before actual search result selection occurs. This model is constructed in advance to estimate the probability that a user will select certain types of results, enabling faster real-time personalization without adding significant latency to the search process.
2Loss of information
If search engines provide all types of search results to users, then the comprehensiveness of information is improved, but the difficulty of selecting relevant results increases
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different result types based on user preferences. Instead of uniformly presenting all results, the system assigns different scores and rankings to results based on the estimated probability of user selection. This allows the most relevant results to be prominently displayed while less relevant results remain accessible but less prominent.
3Measurement precision
If search engines track user behavior to personalize results, then the personalization accuracy is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system changes parameters by transforming raw user behavior data into a probability model with specific parameters that represent user preferences. Instead of directly using complex behavioral data, the system converts it into probabilistic parameters that estimate the likelihood of selecting certain result types, simplifying the personalization process while maintaining accuracy.
Data Source
AI summary
A system tracks a user's search related behavior and constructs a probability model, from the user's search related behavior, to estimate a probability that the user will select a certain type of search result. The system searches sources of information based on a search query received from the user to determine a set of search results and uses the estimated probabilities to select one or more search results from the set of search results.