Contextual Filter Arrangement for Guided Real Estate Search
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Solution Overview
Problem
Conventional real estate search systems require users to manually configure numerous filters, which is time-consuming and cumbersome, often leading to missed properties due to user interface limitations and lack of guidance on relevant filter criteria.
Innovation Solution
A guided search system that suggests and arranges filter criteria based on user interactions and geographic area, using machine learning to predict likely filter criteria and streamline the search process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually configure numerous filters to narrow down property search results, then search precision is improved, but operation time and complexity increase significantly
Solution Approach 1:
The system performs preliminary analysis of user preferences and search history before the actual search operation, pre-configuring relevant filter criteria. This allows the system to present tailored filter suggestions in advance, reducing the time users need to spend manually configuring filters during the actual search process.
Solution Approach 2:
The system automatically analyzes user behavior patterns and search requirements to self-generate relevant filter criteria suggestions. Instead of requiring users to manually configure all filters, the system serves itself by autonomously identifying and presenting the most relevant filters based on accumulated user interaction data and search patterns.
2Measurement precision
If users manually configure numerous filters to narrow down property search results, then search precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system introduces an intermediary layer between the user and the complex filter configuration interface. This intermediary analyzes user intent and automatically translates it into relevant filter criteria suggestions, shielding users from the complexity of numerous filters while maintaining search precision. The intermediary presents only the most relevant filters rather than all available options.
Solution Approach 2:
Instead of presenting all possible filter criteria to users, the system applies partial action by selectively presenting only the most relevant subset of filters based on user preferences and search context. This reduces the operational burden on users while maintaining sufficient search precision through targeted filter suggestions rather than comprehensive manual configuration.
3Measurement precision
If the system provides comprehensive filter options to guide user search, then search quality is improved, but device complexity increases
Solution Approach 1:
The system segments the comprehensive set of filter criteria into multiple hierarchical levels or categories. Instead of presenting all filters simultaneously, the system divides them into relevant groups based on user preferences and search context, managing complexity through structured organization while maintaining access to the full range of filter options when needed.
Data Source
AI summary
A guided search system for suggesting and arranging filter criteria within a user interface for presentation to a user to help guide the user's search for listings is disclosed. The system builds one or more filter criteria frequency data structures indicative of the number of times each filter criterion has been used to filter search results and how often different filter criteria are used together. The system uses the frequency data structures to predict which filter criteria a user will likely employ to narrow their search given the filter criteria the user has already used. The system provides techniques for arranging or rearranging filter criteria within a user interface, by moving, placing, or ordering suggested filter criteria within the user interface, where a user is likely to be able to recognize and interact with the placed filter criteria, based on the determined amounts of use.