Dynamic Session Relevance Model for Search Adaptation
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Solution Overview
Problem
Existing information retrieval systems in session searches often fail to accurately adapt to changes in a user's information need over the course of a search session, leading to suboptimal retrieval of relevant documents.
Innovation Solution
A method that generates a relevance model by combining a session relevance model and a feedback model, which uses historical data and user interactions such as query changes and document impressions to dynamically modify search queries and improve the retrieval of relevant documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static relevance model is used for information retrieval, then the system is simple and fast, but it fails to adapt to changes in user information need during a search session
Solution Approach 1:
The patent implements a dynamic relevance model that evolves during the search session by incorporating query reformulation history and user interactions. The model transitions from a static state to a dynamic state where relevance weights are continuously updated based on observed user behavior patterns, allowing adaptation to changing information needs while maintaining a manageable computational structure through incremental updates rather than complete re-computation.
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring user interactions with search results (clicks, dwell time, reformulations) and using this feedback to iteratively refine the relevance model. The feedback loop captures implicit user preferences and adjusts the relevance weighting accordingly, enabling the system to adapt to user information needs while the feedback processing is integrated into the existing search pipeline to minimize additional complexity.
2Measurement precision
If query reformulation is tracked and analyzed, then user information need can be captured more accurately, but processing overhead and computational cost increase
Solution Approach 1:
The system performs preliminary analysis of query reformulations by maintaining a running history of query changes and pre-computing relevance weight adjustments based on observed reformulation patterns. This preliminary processing allows the system to quickly apply pre-determined weight adjustments during actual search operations, reducing the computational burden during time-critical search execution while still capturing detailed reformulation information for accurate information need estimation.
Solution Approach 2:
The patent implements partial processing of query reformulation data by selectively analyzing only the most informative aspects of reformulations (such as term additions, deletions, and modifications) rather than processing every detail. This selective approach captures sufficient information for accurate information need estimation while avoiding excessive computational overhead from analyzing redundant or less informative elements of query reformulation.
3Reliability
If feedback model is applied to modify queries, then retrieval accuracy improves, but the system may drift from original user intent
Solution Approach 1:
The system applies controlled parameter changes to the query by adjusting relevance weights of specific terms based on the feedback model rather than fundamentally altering the query structure. The feedback model modifies term weights incrementally based on observed user interactions, allowing the system to improve retrieval accuracy through subtle parameter adjustments while preserving the core query intent and avoiding drift from the original user information need.
Data Source
AI summary
A session search relevance model identifies a user's dynamic information need based on a feedback model and a session relevance model. The feedback model is based on query changes in the session search and user interest in particular documents presented throughout the session search. The relevance model modifies a user's current query to retrieve documents most relevant to a user's information need.