Collaborative Search Engine with Weighted Preference Mediation
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Solution Overview
Problem
Group decision-making during search refinement often leads to frustration due to misunderstood inputs and unresolved conflicts, especially when individual preferences and constraints are not adequately considered in collaborative search scenarios.
Innovation Solution
A system that allows multiple group members to contribute to and refine search queries while keeping their preferences private, logically combining queries and incorporating situational awareness, such as time and location, to produce unified search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple group members contribute their preferences to a search, then the relevance and completeness of search results improve, but the complexity of managing and resolving conflicting inputs increases
Solution Approach 1:
The system introduces an automated intermediary mechanism that mediates between conflicting group preferences through weighted scoring and automated query generation. Instead of requiring direct human negotiation, the system acts as an intermediary that processes multiple preferences, applies weighting factors, and generates refined search queries that balance competing interests.
Solution Approach 2:
The system changes parameters by introducing preference weights and confidence levels to quantify and differentiate the strength of various group members' preferences. This parameter transformation converts qualitative preferences into measurable values that can be systematically processed and balanced.
2Measurement precision
If group members openly share their preferences and constraints, then the search can be more accurately tailored to group needs, but some members may feel uncomfortable about their preferences being widely known
Solution Approach 1:
The system segments preference data by assigning unique identifiers and weights to each group member's preferences, allowing the search engine to process individual preferences separately while maintaining overall group coherence. This segmentation enables precise preference incorporation without requiring full transparency of individual constraints.
Solution Approach 2:
The system introduces an intermediary layer that processes preferences anonymously through weighted scoring mechanisms. Individual preferences are submitted through controlled interfaces that do not require public disclosure, and the intermediary system aggregates these preferences into refined search queries without exposing individual inputs.
3Measurement precision
If the search engine processes multiple refined queries sequentially, then the search results become more accurate, but the time required to complete the search increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating preference weights, confidence levels, and query refinement strategies before executing the actual search. Group preferences are processed and weighted in advance, allowing the search engine to execute refined queries more efficiently without sequential bottlenecks.
Solution Approach 2:
The system merges multiple individual preferences and constraints into a single consolidated refined query that incorporates all group inputs simultaneously. This merging approach eliminates the need for sequential processing of individual queries while maintaining the accuracy benefits of considering all preferences.
4Productivity
If the system stores and remembers group preferences for future searches, then future search efficiency improves, but the system complexity and data management requirements increase
Solution Approach 1:
The system implements self-service by automatically storing, weighting, and retrieving group preferences without requiring manual reconfiguration. Once preferences are established, the system serves future searches by automatically applying these stored preferences, reducing both human effort and system complexity over time.
Solution Approach 2:
The system manages complexity by transforming preference data into standardized parameter formats (weights, confidence levels, priority scores) that are easy to store and retrieve. This parameter standardization simplifies data management while enabling efficient application to future searches.
Data Source
AI summary
Each user can contribute to an original search and to refining the results of the search. Preferences of all of the users are considered in the search even while those preferences can be kept private. In some embodiments of the present invention, at least two users each submit a search query. The multiple search queries are logically combined to produce one set of search results. The results can be reviewed by the users and refined if necessary. In some embodiments, a search query can be specified by a single user, but the search engine takes into consideration the stored preferences of multiple users (in addition to the search query itself, of course).