Query Refinement via Peer Activity Signals
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Solution Overview
Problem
Network-based services often fail to provide all relevant results to users due to limitations in search capabilities, which can't encapsulate all possible combinations of criteria, leading to unawareness of additional or alternative items, especially in volatile inventory environments like travel services where users may miss more desirable options.
Innovation Solution
Facilitating the generation of new search queries based on current queries, user activities, and information from other users, including recommendations and assertions that suggest alternative options, such as non-stop flights or alternate destinations, to refine or expand search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If search capabilities are expanded to cover all possible combinations of criteria, then completeness of search results is improved, but device complexity and processing requirements increase significantly
Solution Approach 1:
The system performs preliminary actions by analyzing user behavior patterns, booking trends, and activity data before the user completes their search. It proactively generates recommendations and alternative queries based on pre-analyzed data, reducing the need for users to manually explore all possible search combinations while maintaining result completeness
Solution Approach 2:
The system introduces an intermediary layer between the user and the comprehensive search results. This intermediary analyzes user behavior data and selectively presents relevant alternatives and recommendations, filtering the vast search space into manageable, personalized suggestions without requiring the system to display all possible combinations
2Reliability
If volatile inventory items are monitored continuously for availability changes, then reliability of item availability information is improved, but use of energy and processing resources increases
Solution Approach 1:
The system enables self-service monitoring where users can set up their own alerts and notifications for items they are interested in. The system automatically monitors inventory changes for these user-selected items and notifies users when availability changes occur, distributing the monitoring burden and reducing overall system energy consumption while maintaining reliability for high-priority items
Solution Approach 2:
Instead of continuously monitoring all inventory items in the system, the system applies partial monitoring focused on items that are most relevant to current user searches and bookings. It monitors inventory changes selectively for high-demand or recently searched items, providing sufficient reliability for critical items while reducing energy consumption across the entire inventory
3Adaptability or versatility
If more search criteria and filters are provided to users, then adaptability of search options is improved, but ease of operation decreases due to overwhelming choices
Solution Approach 1:
The system applies local quality by providing different levels of search complexity to different users based on their behavior patterns and preferences. For users who prefer simplicity, the system presents only the most relevant filters and options. For power users who need more control, additional advanced filters and criteria are made available. This localized approach to interface complexity maintains versatility while preserving ease of operation for each user segment
Solution Approach 2:
The system performs preliminary analysis of user preferences and behavior to pre-configure search parameters and filters. Based on this preliminary action, the system automatically adjusts which search options are displayed and how they are organized, presenting only the most relevant criteria to each user. This eliminates the need for users to navigate through all possible filters while maintaining access to comprehensive search capabilities when needed
4Productivity
If user behavior data is collected and analyzed to generate recommendations, then productivity of item discovery is improved, but loss of user privacy information increases
Solution Approach 1:
The system extracts only the specific behavioral patterns and preferences that are necessary for generating recommendations, rather than collecting and storing all user data. It selectively extracts information about search patterns, booking preferences, and item interests while leaving out sensitive personal information. This extraction approach maintains productivity in item discovery while minimizing privacy information loss
Data Source
AI summary
A network-based service may be provided for facilitating queries for a number of items, such as travel services. A user may submit a query including criteria for determining one or more relevant items. Based on the submitted query, the network-based service may present the user with information regarding the actions of other similar users of the network-based service, such as searches performed by the other users. Based on this information, the user may elect to supplement the current query to conform to the actions for other users. In some embodiments, actions by other users may be based at least in part on a category of the querying user. By presenting actions of similar users, a current user may be enabled to select the most relevant query terms for identifying a desired item.