Conversational Bot for Dynamic Search Filter Adjustment
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Solution Overview
Problem
Online shopping platforms face challenges in providing users with intuitive and efficient ways to navigate through electronic catalogs, often resulting in frustration due to the time-consuming process of refining search results, which can lead to sub-optimal outcomes.
Innovation Solution
Implementing an automated conversation bot that engages users to identify undesirable attributes of items, processes user feedback to update filters, and applies these changes to search engine results, allowing users to easily navigate back up the search funnel by relaxing relevant filters while maintaining preferred ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users refine search results by providing more filter requirements, then search precision improves, but user time and effort increase
Solution Approach 1:
The system implements feedback loops where the bot continuously monitors user interactions with search results, analyzes engagement patterns, and automatically adjusts filter parameters based on this feedback. This allows the system to learn from user behavior and refine searches without requiring explicit user input for each adjustment, thereby maintaining high precision while reducing time investment.
Solution Approach 2:
The conversational bot autonomously performs search optimization tasks by automatically analyzing user preferences, adjusting filters, and refining result sets without requiring continuous user intervention. The system serves itself by using its own resources (bot capabilities, data processing) to improve search outcomes, freeing users from the time-consuming manual filter adjustment process.
2Measurement precision
If users manually adjust filters to navigate the search funnel, then search accuracy improves, but ease of operation deteriorates
Solution Approach 1:
The conversational bot acts as an intermediary between the user and the complex filter adjustment process. Instead of requiring users to directly manipulate multiple filter parameters, the bot translates user intent into appropriate filter adjustments, managing the complexity behind the scenes while maintaining search accuracy.
Solution Approach 2:
The system enables users to simply express their needs in natural language, while the bot autonomously handles the complex task of translating these expressions into precise filter configurations and search queries, making the operation extremely easy while maintaining high accuracy.
3Measurement precision
If the search funnel narrows to specific results, then result relevance improves, but adaptability deteriorates
Solution Approach 1:
The search system dynamically adjusts between narrow and broad filter applications based on real-time user feedback and interaction patterns. The bot can expand or contract the search funnel width as needed, allowing the system to adapt its precision level dynamically rather than being locked into a fixed narrow or broad state, thus maintaining both relevance and adaptability.
Solution Approach 2:
The system changes filter parameters dynamically based on user needs, allowing transition between narrow and broad search states. The bot adjusts parameter values and filter configurations in response to user feedback, enabling the search to adapt its precision level and explore different result spaces as required, maintaining both relevance and versatility.
4Quantity of substance
If users start with broad searches and add requirements, then comprehensive coverage improves, but productivity deteriorates
Solution Approach 1:
The bot performs preliminary analysis of user needs and pre-configures appropriate filter settings before executing the search. By anticipating which filters and parameters will be most relevant based on initial user input, the system prepares an optimized search configuration in advance, reducing the time required for iterative filter adjustment while maintaining comprehensive coverage of relevant results.
Solution Approach 2:
The system implements continuous feedback mechanisms where the bot monitors user interactions with search results and automatically adjusts the search scope and filter parameters. This feedback-driven approach allows the system to maintain comprehensive coverage of relevant results while efficiently navigating the search space, improving productivity by eliminating manual iterative adjustments.
Data Source
AI summary
Described herein are a system and methods for providing relevant search results to a user using an automated user assistant to update filters used as search parameters. In some embodiments, an automated user assistant may initiate a conversation with a user detected interacting with an item. The user may provide feedback that indicates one or more attributes relevant to the user's interests with respect to the currently viewed item. In some embodiments, the user may also provide an indication of how values associated with those attributes would be made more relevant to the user. Various filters associated with the attributes may be updated based on the received user feedback. The updated filters may then be provided as search parameters to a search engine. A set of search results returned by the search engine may be provided to the user.