Personalized Car Recommendations via Web Traffic Tagging
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Solution Overview
Problem
Current systems for providing automobile information to users are inadequate in terms of quality, as they fail to accurately deliver relevant recommendations based on user preferences and search history.
Innovation Solution
A computer-implemented method and apparatus that aggregates automobile data from user web pages, compares it to a set of tags generated using a frequency-based machine learning model, and transmits personalized automobile suggestions to users, leveraging both user search data and expert reviews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current search engine systems are used to deliver automobile information, then information delivery is provided, but the quality of recommendations is inadequate and not personalized to user preferences
Solution Approach 1:
The system performs preliminary actions by aggregating automobile data from multiple web pages and generating tags from expert reviews before the user makes a purchase decision. This advance preparation of personalized recommendation data enables accurate suggestions when the user is ready to buy, resolving the contradiction between recommendation accuracy and personalization capability.
Solution Approach 2:
The patent introduces an intermediary machine learning model that processes and analyzes automobile data from various sources. This intermediary system bridges the gap between raw data and personalized recommendations, enabling the system to deliver both accurate and personalized automobile suggestions by mediating between data aggregation and user-specific recommendation needs.
2Measurement precision
If comprehensive automobile data is aggregated from multiple web pages, then recommendation quality improves, but data processing complexity increases
Solution Approach 1:
The system extracts only the essential and relevant features from aggregated automobile data by generating tags from expert reviews. This extraction process separates critical information from the comprehensive data set, maintaining recommendation accuracy while reducing processing complexity by focusing on key characteristics rather than processing all raw data.
Solution Approach 2:
The patent transforms the comprehensive automobile data into a different parameter representation through tag generation. By changing the data parameters from raw web page content to structured tags derived from expert reviews, the system maintains information quality while simplifying the data structure for more efficient processing and recommendation generation.
Data Source
AI summary
One or more embodiments are generally directed to techniques to provide specific vehicle recommendations. Various techniques, methods, systems, and apparatuses include utilizing user web-traffic and/or one or more tags generated by application of a machine learning model to a data source, where the data source may include language with respect to one or more automobiles or vehicles, to provide a recommendation for a particular automobile.