Dynamic Property Recommendations From Machine-Learned User Profiles
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Solution Overview
Problem
Existing real estate digital products fail to personalize property information based on user preferences and adapt dynamically to changing user needs, providing static and buyer-agnostic information that does not account for individual user characteristics or life events.
Innovation Solution
Utilizing machine learning algorithms to classify users into categories and groups based on weighted variables, forming a dynamic preference profile that adapts to user changes over time, and recommending properties by comparing user profiles with neighborhood and property profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static real estate information is presented to all buyers uniformly, then the system is simple to operate and requires minimal data processing, but the information cannot be personalized or tailored to individual buyer preferences
Solution Approach 1:
The patent implements dynamic user profiles that adapt and evolve based on user interactions, preferences, and behavior patterns. The system transitions from static information presentation to dynamic personalization by continuously updating user profiles with new data and adjusting recommendations accordingly, allowing the system to adapt to changing user needs over time
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions, preferences, and behavior patterns are continuously collected and fed back into the machine learning models. This feedback loop enables the system to learn from user responses and improve personalization accuracy, resolving the contradiction by using feedback-driven adaptation to enhance personalization while managing complexity through iterative learning
2Adaptability or versatility
If machine learning algorithms with multiple variables are used to classify users and create preference profiles, then personalized property recommendations can be generated, but the computational complexity and data processing requirements increase significantly
Solution Approach 1:
The patent segments the user classification process into multiple hierarchical levels, including user categories, user groups, and individual preference profiles. Each segment processes specific aspects of user preferences using targeted machine learning models, reducing overall computational complexity by dividing the problem into manageable pieces that can be processed independently and efficiently
Solution Approach 2:
The system dynamically adjusts the number and weight of variables in preference profiles based on user characteristics and data availability. Rather than applying the same complex multi-variable model to all users, the system adapts parameter complexity to match individual needs, reducing computational burden for users with simpler preferences while maintaining high accuracy for those requiring detailed personalization
3Reliability
If the system continuously updates user preferences based on life events and feedback, then the recommendations remain current and relevant, but the system requires ongoing data collection and processing
Solution Approach 1:
The patent implements preliminary classification of users into categories and groups based on initial data, establishing baseline preference profiles before detailed property recommendations are generated. This preliminary action reduces the computational burden of continuous updates by maintaining stable hierarchical classifications while only updating specific preference parameters in response to life events, thereby maintaining recommendation relevance while reducing ongoing processing time
Solution Approach 2:
The system employs periodic updates of user profiles triggered by specific life events or time intervals rather than continuous real-time processing. This periodic action maintains recommendation relevance by updating preferences at meaningful intervals while significantly reducing overall data processing time compared to continuous updates, balancing reliability with efficiency
Data Source
AI summary
Methods, apparatus, and systems are disclosed that determine property recommendations for a user. The methods, apparatus, and systems may also predict when a user is likely to become a property buyer and/or seller to determine when certain property recommendations are to be made. The methods, apparatus, and systems use one or more machine learning algorithms to determine a dynamic preference profile for a user that accurately reflects the user and how the user changes over time. The preference profile takes into account property-related information and user characteristic information as variables for the machine learning algorithm(s).