Latent Dirichlet Allocation for Website Intent Detection
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Solution Overview
Problem
Current information handling systems lack the ability to effectively utilize user intent to enhance web experiences, search results, and website design, leading to inefficient navigation and recommendation processes.
Innovation Solution
Implementing a method that uses latent Dirichlet allocation (LDA) to analyze user interactions, such as page views and search queries, to determine user intent, and subsequently improve search results, recommendations, and website design by correlating user interests with webpage content, thereby personalizing the user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional information handling systems are used to process web information, then basic information processing can be performed, but the system cannot effectively utilize user intent to enhance web experiences
Solution Approach 1:
The patent introduces latent Dirichlet allocation (LDA) as an intermediary computational model that bridges user interactions and intent determination. LDA serves as a mediator that transforms raw navigation data into meaningful user intent representations, enabling the system to adapt to user preferences without requiring complex rule-based systems or manual configuration.
Solution Approach 2:
The patent replaces traditional mechanical information processing methods with statistical modeling and machine learning techniques. Instead of using rigid if-then rules or manual categorization systems, the patent employs probabilistic topic modeling to automatically infer user intent from behavioral patterns, substituting mechanical processing with intelligent computational approaches.
2Productivity
If user intent analysis is implemented to personalize user experience, then user engagement is enhanced, but computational resources and processing time are increased
Solution Approach 1:
The patent performs user intent analysis in advance by continuously modeling user preferences based on historical navigation data. By pre-computing user intent profiles and maintaining updated models of user preferences, the system avoids performing intensive computations in real-time during user interactions, thereby reducing immediate computational resource consumption while maintaining high personalization quality.
Solution Approach 2:
The patent transforms the complex problem of real-time user intent analysis into a more manageable form by changing the computational parameters. Instead of analyzing all possible user behaviors simultaneously, the patent uses topic modeling to reduce the dimensionality of user preference space into discrete topics, making the personalization process computationally efficient while preserving the essential characteristics of user intent.
3Measurement precision
If comprehensive user interaction data is collected to determine user intent, then search result relevance is improved, but data privacy concerns and storage requirements increase
Solution Approach 1:
The patent extracts only the essential features from comprehensive user interaction data that are necessary for determining user intent. Instead of storing and processing all raw navigation data, the patent uses topic modeling to extract latent thematic patterns from user behavior, retaining only the critical information needed for personalization while discarding redundant data, thereby reducing storage requirements while maintaining measurement precision.
Data Source
AI summary
A method for using evaluation of intent to improve website usability includes gathering page text and input text from pages viewed by a user, generating a word set from the page text and input text, and performing latent dirichlet allocation modeling on the word set to evaluate user intent. The intent can be used to provide recommendations, improve search results, or identify weaknesses in a website design.