User-Specific Context Integration for Enterprise Search
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Solution Overview
Problem
Traditional search methodologies in enterprise platforms fail to provide user-specific, contextually relevant search results due to differences in user perspectives and behaviors, leading to irrelevant search outcomes.
Innovation Solution
Implementing a system that uses learned user profiles to apply profile context weights to search results, ranking and arranging them based on user-specific digital content item sets produced by integrating user behavior data and role information within the enterprise platform, utilizing models like term vector and multiple additive regression trees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search methodologies are used in enterprise platforms, then the search system is simple and easy to implement, but the search results are not contextually relevant to individual users
Solution Approach 1:
The search system is segmented into multiple independent components: a first model for retrieving preliminary search results, a second model for applying user-specific context, and a learned user profile system. This segmentation allows each component to specialize in one aspect of the search process, improving overall relevance without requiring complete system redesign.
Solution Approach 2:
A learned user profile acts as an intermediary between the traditional search system and the user. The profile stores contextual information about user behavior, preferences, and role, mediating between the generic search results and individual user needs. This intermediary enables personalized ranking without requiring fundamental changes to the underlying search infrastructure.
2Measurement precision
If user-specific context integration is implemented, then the relevance of search results to individual users improves, but the computational resources and processing time increase
Solution Approach 1:
User profiles and contextual information are pre-computed and stored before search operations occur. The system learns user behaviors, preferences, and role characteristics in advance, so that during actual search operations, only lightweight profile retrieval and weighting operations are needed. This preliminary action significantly reduces real-time computational requirements.
Solution Approach 2:
The system applies profile context weights selectively to search results rather than processing all possible user attributes equally. By focusing computational effort on the most relevant profile dimensions for each search context, the system achieves good personalization with reduced computational overhead compared to exhaustive analysis of all user data.
3Measurement precision
If multiple models are applied for search and ranking, then the accuracy of user-specific digital content item set improves, but the device complexity and implementation difficulty increase
Solution Approach 1:
The search system is divided into two distinct models with specific responsibilities: the first model handles information retrieval and preliminary result generation, while the second model handles ranking and re-ranking based on user context. This clear segmentation allows each model to be optimized for its specific function and simplifies the overall system architecture compared to a single monolithic model.
Solution Approach 2:
The learned user profile serves multiple functions across different search operations and contexts. It is used for both retrieving relevant results and ranking them appropriately, making it a universal component that works across various search scenarios without requiring separate mechanisms for each function.
Data Source
AI summary
Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for integrating user-specific context indicators into a searchable enterprise platform. In one embodiment, an apparatus is configured to apply a learned user profile, to a set of search results to obtain a user-specific, ranked arrangement of search results. The learned user profile may be developed through the application of a machine learning and/or trained model to a set of user behaviors that have been determined or otherwise detected within an enterprise platform, such that the user-specific context in which a user's search arises can be modeled and applied to retrieved digital content items associated with a search query within the enterprise platform.