Transition Probability Calculation for User Activity Indications
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Solution Overview
Problem
Existing systems lack an effective method to determine transition probabilities between user interactions with documents and queries, leading to suboptimal information retrieval and recommendation processes.
Innovation Solution
The method involves identifying sets of user activity indications, determining first and second transition probabilities from these sets to subsequent sets, and calculating a user transition probability based on these probabilities to rank and select relevant information, such as queries or documents, for presentation to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transition probabilities between user interactions are not determined, then information retrieval systems can operate with simpler methods, but the relevance and accuracy of retrieved information deteriorates
Solution Approach 1:
The patent segments user interaction data into distinct activity indications (document views, queries issued) and calculates transition probabilities between these segmented activities. This segmentation allows the system to build probability models from discrete, manageable interaction units rather than treating user behavior as a continuous complex signal, thereby improving prediction accuracy while keeping the computational approach tractable
Solution Approach 2:
The system performs preliminary analysis by collecting and processing user interaction data to pre-calculate transition probabilities before actual information retrieval occurs. This preliminary action creates a ready-to-use probability model that can quickly rank and select information during actual retrieval operations, improving both accuracy and operational efficiency
2Reliability
If multiple transition probabilities are calculated from different activity indications, then the prediction of user interests improves, but the computational complexity and processing time increases
Solution Approach 1:
The patent merges multiple transition probabilities calculated from different activity indications (document views, queries) into a unified probability model. By combining these multiple probability estimates, the system achieves more reliable and relevant information retrieval predictions, as the merged model captures diverse aspects of user behavior patterns rather than relying on a single probability source
3Productivity
If transition probability determination is implemented, then the ranking and selection of information improves, but the system complexity and resource requirements worsen
Solution Approach 1:
The system implements self-service by automatically collecting user interaction data, calculating transition probabilities, and using these probabilities to rank and select information without requiring manual intervention. This automation improves information retrieval efficiency while managing system complexity through programmatic processing rather than manual analysis methods
Data Source
AI summary
Methods and apparatus related to determining a transition probability related to transition from one or more past activity indications to one or more subsequent activity indications. Some implementations of the specification are directed to methods and apparatus related to identifying a set of one or more activity indications of a user, identifying at least first and second transition probabilities from the set to a subsequent set of one or more activity indications, and determining a user transition probability from the set to the subsequent set based on the first and second transition probabilities.