Context-Aware Search Using Hidden Markov Models
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Solution Overview
Problem
Conventional desktop and web search systems fail to provide adequate personalization of search results, as they do not consider user preferences or contexts beyond query strings and simple clickthrough data, leading to irrelevant rankings.
Innovation Solution
The development of a context-aware search system that generates task-driven user models using Hidden Markov Models (HMMs) to analyze user activity logs, detect bursts of activity, and map them to tasks, thereby providing personalized query processing and ranking of search results based on user behavior and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional desktop search systems use keyword searches that rank results by relevance to query, then search results are provided based on query matching, but user preferences and personalized results are not considered
Solution Approach 1:
The system performs preliminary actions by detecting bursts of user activity and mapping them to tasks in advance, generating a user model before query processing occurs. This allows the system to have personalized search capabilities ready when users submit queries, rather than computing personalization on-demand
Solution Approach 2:
The patent introduces a user model as an intermediary between the search system and user queries. This user model, generated from activity burst detection and task mapping, serves as a mediator that captures user preferences and contextual information, enabling personalized search without requiring direct analysis of user behavior during query processing
2Measurement precision
If web search systems model query contexts by mining searches or browsing logs, then some personalization is achieved, but adequate personalization fails because only query strings and simple clickthrough data are considered
Solution Approach 1:
The patent segments user activity into distinct bursts of activity, where each burst represents a meaningful unit of user behavior. By detecting and analyzing these activity bursts separately, the system can understand user context more precisely without being overwhelmed by the entire browsing history
Solution Approach 2:
The system dynamically generates user models based on detected activity bursts and mapped tasks. Rather than using static user profiles, the user model adapts to reflect current user context and preferences, allowing the system to capture evolving user behavior patterns
3Ease of operation
If conventional search systems rank results according to preferences of most users or keyword relevance, then general applicability is maintained, but personalized and relevant results for individual users are not provided
Solution Approach 1:
The patent applies local quality by providing different search results based on individual user characteristics. Instead of uniform ranking for all users, the system tailors search results to each user's detected tasks and preferences, making the search experience locally optimized for each user while maintaining overall system functionality
Data Source
AI summary
A processing device of an information processing system is configured to detect bursts of activity associated with a particular user, to map the bursts to tasks performed by the user, to generate a user model based on the mapping of bursts to tasks, and to provide personalized query processing to the user based on the user model. The user model may comprise a Hidden Markov Model. In one embodiment, a query is received from the user in a current time window, and a model parameter is obtained. Based on the model parameter, a most likely task for the user in the current time window is identified, and based on the query, the most likely task, and user classification, search results are ranked for presentation to the user. The processing device may be configured to communicate the user model to a cloud service provider, and to receive from the cloud service provider the ranked search results determined based on the user model.