Personalized Building Improvement Recommendations via User Profiling
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Solution Overview
Problem
Existing building improvement information is often generic, misleading, and irrelevant, failing to provide personalized and accurate recommendations due to lack of consideration for user-specific factors such as building characteristics, environmental conditions, and user motivations, leading to user dissatisfaction and ineffective energy-saving suggestions.
Innovation Solution
A system that generates personalized building improvement content by creating a user profile combining building and behavioral data, using a physics-based model to predict energy consumption and a recommendation personalization engine to filter and prioritize recommendations based on user-specific factors, ensuring relevance and trustworthiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic building improvement recommendations are provided, then information coverage is broad, but relevance to individual users deteriorates
Solution Approach 1:
The system segments the generic recommendation list into personalized subsets by creating user profiles that categorize users based on building characteristics, energy consumption patterns, and behavioral factors. This segmentation allows the system to deliver targeted recommendations that are relevant to each user's specific situation while maintaining broad information coverage across different user segments.
Solution Approach 2:
The system performs preliminary actions by pre-collecting and analyzing user data (building characteristics, energy consumption, behavioral patterns) before generating recommendations. User profiles are created in advance, enabling the system to quickly retrieve and present highly relevant recommendations without requiring users to sift through generic information.
2Loss of information
If comprehensive building improvement information is provided, then information completeness is improved, but user effort to find relevant information increases
Solution Approach 1:
The system uses feedback mechanisms by analyzing user interactions, behavioral patterns, and response data to continuously refine and personalize recommendation lists. This feedback loop allows the system to maintain comprehensive information while automatically filtering and prioritizing content based on individual user needs, thereby reducing the effort users must expend to find relevant information.
Solution Approach 2:
The system performs self-service by automatically analyzing user data, creating profiles, and generating personalized recommendation lists without requiring user intervention. The system autonomously filters and organizes comprehensive building improvement information according to each user's specific characteristics, eliminating the need for users to manually search through extensive information.
3Device complexity
If building improvement recommendations are provided without personalization, then system complexity is low, but recommendation accuracy deteriorates
Solution Approach 1:
The system applies parameter changes by transforming generic recommendation parameters into personalized ones based on user profile data. Building characteristics, energy consumption patterns, and behavioral factors serve as varying parameters that modify the standard recommendation set, enabling accurate personalized recommendations without requiring a completely complex new system architecture.
4Ease of manufacture
If generic saving estimates are provided, then information generation is simple, but trustworthiness deteriorates due to over- or underestimation
Solution Approach 1:
The system applies local quality by providing customized saving estimates tailored to each user's specific building characteristics and energy consumption patterns rather than generic estimates. This localized approach maintains reasonable simplicity in the estimation process while significantly improving trustworthiness by accounting for individual user factors that affect actual energy savings.
Data Source
AI summary
The technology disclosed herein provides accurate, targeted, building improvement content in a way that resonates with the user by considering the user's whole ecosystem. The technology uses details of the user's home, neighborhood, family, environmental and historical factors, goals, economic situation, and motivations and preferences to tailor content to a user's personal situation. A server or other computing device may accomplish this by receiving data from the client, a third party, or data local to the server; building modeling constructs based on these data sets such as a physics-based model of the building and a behavioral model of the user; operating these models relative to possible discrete building improvement content units; and using the results to determine personalized building improvement content for the user such as, for example, a webpage.