Ranking Query Results Using External Context
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Solution Overview
Problem
Users in enterprise settings face difficulties in searching for documents created by others or associated with projects and frequently visited sites, as existing search systems require users to know the specific location of the documents, limiting the effectiveness of searches based on external user context.
Innovation Solution
A search service that receives a query and an external context list, determines relevance values for each entry, and ranks query results by matching these entries against associated properties of items, using a search index to compute scores and prioritize results based on relevance, allowing users to search for documents without knowing their exact location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users search for documents in specific locations (folders or collaboration sites), then the search system can locate documents, but users must know and remember the exact location of documents which reduces ease of operation
Solution Approach 1:
The system pre-computes and stores relevance scores for documents based on external user context (collaborators, projects, frequently visited sites) before queries are submitted. This preliminary ranking allows the search service to quickly retrieve and present relevant documents without requiring users to know specific locations, resolving the contradiction between ease of operation and location information requirements
Solution Approach 2:
The patent introduces an intermediary relevance scoring mechanism that bridges the gap between user queries and document locations. Instead of directly searching for documents in specific folders, the system uses external user context as an intermediary to rank and present relevant documents, eliminating the need for users to remember document locations while maintaining search effectiveness
2Reliability
If the search system processes all documents in the enterprise, then comprehensive search results are achieved, but processor and memory usage increase significantly
Solution Approach 1:
The system applies local quality by computing relevance scores differently for different documents based on their association with external user context. Documents are ranked according to their specific relevance to the user's collaborators, projects, and frequently visited sites, rather than treating all documents uniformly. This selective approach ensures comprehensive search results for relevant documents while avoiding unnecessary processing of unrelated documents, thus reducing processor and memory usage
Solution Approach 2:
The patent changes the parameter of document relevance from a binary (relevant/not relevant) state to a continuous relevance score based on external user context. By computing and storing these relevance scores as a new parameter, the system can efficiently filter and rank documents during search operations, achieving comprehensive results for relevant documents without processing the entire document corpus, thereby reducing computational resource consumption
Data Source
AI summary
Users are most likely to search for documents “around” them, such as documents which are created, read, or modified by other users they work closely or collaborate with, documents on sites the user frequents, and/or documents trending around them, in addition to documents they've created or interacted with themselves. Therefore, embodiments are directed to relevance ranking of query results based on external context. A search service may receive a query and an external context list, determine a relevance value for each entry in the external context list, and perform the query, where a search index comprising items may be returned in response to performance of the query. The search service may then match entries in the external context list against associated properties of the items, compute a score for the items based on the matching, and rank results of the query based on the score computed for the items.