Shared Real Estate Recommendations for Multi-User Preference Merging
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Solution Overview
Problem
Existing real estate listing systems fail to effectively incorporate social decision-making, as property recommendations are typically made by a family with a real estate agent's assistance rather than the buyer alone, lacking personalized and accurate suggestions based on multiple users' interactions.
Innovation Solution
A system that allows users to share their real estate browsing history and interactions with others, enabling a recommendations engine to merge preferences and provide personalized recommendations by incorporating explicit and implicit actions from multiple users, including features like shared lists and browsing sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real estate recommendations are made based on a single user's browsing history, then the system is simple to operate, but the recommendation accuracy and personalization are insufficient for family decision-making
Solution Approach 1:
The patent merges multiple users' browsing histories and preferences into a unified recommendation system. The system combines explicit preferences (user inputs) and implicit preferences (browsing behavior) from multiple family members to generate consolidated recommendations, thereby improving recommendation accuracy for group decision-making while managing system complexity through automated processing
2Adaptability or versatility
If the system incorporates multiple users' browsing histories and interactions, then the recommendation personalization improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the recommendation system into distinct modules: one for collecting explicit preferences, another for tracking implicit browsing behavior, and a third for merging and processing these data streams. This segmentation allows the system to handle multiple users' data independently before consolidation, reducing overall processing complexity while maintaining high personalization capabilities
Solution Approach 2:
The system introduces an intermediary processing layer that mediates between raw user interaction data and final recommendations. This intermediary layer aggregates and reconciles conflicting preferences from multiple users, applying weighting algorithms and conflict resolution mechanisms to produce balanced, personalized recommendations without requiring complex direct processing of all raw data
3Reliability
If the system tracks both explicit and implicit user actions, then the recommendation quality improves, but the information processing load increases
Solution Approach 1:
The patent applies partial action by selectively processing only the most significant explicit and implicit user actions rather than all possible interactions. The system identifies and weights key preference indicators (such as saved properties, time spent on listings, explicit likes/dislikes) while filtering out less relevant data, thereby maintaining high recommendation quality without overwhelming information processing loads
Data Source
AI summary
A system for recommending comprises an interface and a processor. An interface is configured to receive an input. The input is stored and associated with two user identifiers. The processor is configured to make recommendations based at least in part on the input.