Document Embedding for Footer Links and Question Recommendations
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Solution Overview
Problem
Current search engines and tutor matching websites lack efficient methods to automatically generate relevant subject matter links and recommend previously answered questions based on user queries, leading to suboptimal user experience and SEO performance.
Innovation Solution
The implementation of document embedding algorithms like word2vec, doc2vec, or GloVe to automatically generate footer links and recommend previously answered questions by analyzing user queries and determining related subject matter, enhancing SEO and user relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to create footer links and question recommendations, then customization and relevance can be improved, but time consumption and labor costs increase significantly
Solution Approach 1:
The system automatically generates footer links and question recommendations by processing user queries through document embedding algorithms. The website serves itself by extracting related subject matter from query data and autonomously populating footer links and recommendation sections without manual intervention, thus achieving both high relevance and time efficiency
Solution Approach 2:
The patent replaces manual mechanical processes of creating footer links and selecting recommended questions with automated computational processes. Document embedding algorithms and vector space models compute relevant subject matter automatically, substituting human effort with machine-based semantic analysis and data processing
2Productivity
If document embedding algorithms are used to automatically generate footer links and question recommendations, then productivity and time efficiency are improved, but system complexity increases
Solution Approach 1:
The document embedding algorithm serves multiple functions simultaneously: it generates footer links, provides question recommendations, and adapts to different query types. This multi-functionality justifies the initial complexity investment by delivering diverse outputs from a single computational core, improving overall productivity across multiple website features
Solution Approach 2:
The system manages complexity by adjusting parameters such as vector dimensionality, similarity thresholds, and processing batch sizes. These parameter changes allow optimization of algorithm performance and resource consumption, making the complex system adaptable to different operational requirements and hardware capabilities
3Reliability
If SEO optimization is prioritized by creating more footer links, then search engine ranking improves, but page load time and user interface complexity increase
Solution Approach 1:
The system optimizes footer links by applying different qualities to different links based on their relevance scores. High-relevance links receive prominent placement and active hyperlink status, while lower-relevance links may be placed less prominently or marked as inactive. This local differentiation maintains SEO benefits while managing page complexity and load requirements
Data Source
AI summary
A system and method for utilizing automatically generating related subject matter areas to create footer links for a subject matter page of a tutor matching website is disclosed and claimed. In particular, a document embedding algorithm such as word2vec, doc2vec or GloVe can be trained using on a particular subject using pertinent material such as, for example, textbooks, learned papers, and transcripts of lectures. Once trained, the document embedding algorithm can be used to generate a list of related subjects that can be used to automatically build footer links for a particular subject matter page. The related footer links can improve the positioning of the tutor matching website with common Internet search engines. The same document embedding algorithm can be used to identify previously answered questions that may assist a user posing a question to the tutor matching website.