Residence Recommendation System Using Interest Proximity Scoring
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Solution Overview
Problem
Current residence search systems fail to provide personalized recommendations based on users' personal interests and lifestyle, relying solely on location, budget, and amenities without considering the user's preferences for nearby activities and community dynamics.
Innovation Solution
A method utilizing processors to determine users' interests and preferences, calculating scores for potential residences based on proximity to interest locations, and generating signals for personalized recommendations, incorporating data from various sources, including social media and external databases, to tailor housing searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional residence search systems are used that rely on location, budget, and amenities, then the search process is simple and straightforward, but the recommendations are not personalized and do not consider user interests and lifestyle
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user interest data from multiple sources (social media, browsing history, surveys) before generating residence recommendations. This advance preparation of user profile information enables personalized recommendations without adding complexity to the actual search process, as the personalization data is already prepared in advance.
Solution Approach 2:
The patent introduces an intermediary component - the user profile system - that mediates between the user's implicit interests and the residence recommendations. This intermediary processes and structures user data into actionable insights, allowing the system to provide personalized recommendations without requiring direct complex interactions between all system components.
2Measurement precision
If residence recommendations are generated without considering personal interests, then the system operation is fast and efficient, but the relevance and accuracy of recommendations are low
Solution Approach 1:
The system conducts preliminary analysis of user interests and creates detailed user profiles before the residence search process. By pre-processing and storing user preference data in structured formats, the system can quickly retrieve and apply this information during recommendation generation, thereby improving accuracy without significantly increasing processing time during the actual search.
Solution Approach 2:
The user profile system is dynamic and adapts to user preferences in real-time. As users interact with the system or provide new information, the profile updates automatically, allowing the recommendation accuracy to improve continuously without requiring reprocessing of all underlying data, thus maintaining efficiency while enhancing precision.
3Adaptability or versatility
If comprehensive user data is collected from multiple sources to personalize recommendations, then the personalization quality is high, but the data privacy and security risks increase
Solution Approach 1:
The system extracts only the necessary and relevant user data elements needed for personalization while leaving unnecessary personal information separate or aggregated. By selectively extracting specific interest indicators rather than storing complete user profiles, the system maintains personalization quality while reducing privacy risks associated with storing excessive personal data.
Solution Approach 2:
The patent employs an intermediary data processing layer that acts as a buffer between raw user data and the recommendation system. This intermediary anonymizes, aggregates, or transforms personal information into usage patterns and interest categories, enabling high-quality personalization while protecting user privacy by preventing direct access to sensitive personal data.
Data Source
AI summary
Embodiments for providing residence recommendations by one or more processors are described. At least one interest associated with a user is determined. At least one interest location associated with the at least one interest is identified. A score for each of a plurality of potential residences for the user is calculated at least based on a distance between the respective potential residence and each of the at least one interest locations. A signal representative of the calculated score for each of the plurality of potential residences is generated.