Search Term Modification via User Activity Analysis
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Solution Overview
Problem
Current online search methods overwhelm users with irrelevant results due to broad or narrow search terms, and existing search engines lack the ability to determine user interests outside the search application, making it difficult to retrieve relevant information efficiently, especially on resource-constrained devices like smartphones.
Innovation Solution
A method that analyzes user activity from various applications to identify interests and modify search terms by adding context-relevant modifiers, ensuring search results are relevant to the user's interests and highlighting mandatory terms for quicker result selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a user performs an online search using a search application, then information can be retrieved from electronic data, but the user is overwhelmed by hundreds or thousands of irrelevant results that require significant time and computing resources to process
Solution Approach 1:
The system performs preliminary analysis of user activity data from multiple applications before the search is executed. This includes identifying user interests, frequently used modifiers, and contextual information in advance, so that when a search is performed, the results can be immediately filtered and ranked according to pre-computed relevance metrics, eliminating the need for users to manually process irrelevant results
Solution Approach 2:
The system continuously monitors and analyzes user interaction patterns across applications, using this feedback to dynamically update user interest profiles and search result ranking algorithms. This feedback loop ensures that search results become progressively more relevant over time, reducing the time users spend filtering results while improving information retrieval accuracy
2Quantity of substance
If a search engine returns comprehensive results for a search term, then more potential relevant information is available, but the volume of data overwhelms the user and requires excessive computing resources to process
Solution Approach 1:
Instead of uniformly treating all search results, the system applies different quality filters and ranking criteria to different portions of the result set based on user-specific characteristics. Results are locally optimized for each user by applying personalized interest profiles, device resource constraints, and contextual information to determine which results receive priority processing and display prominence
Solution Approach 2:
The system dynamically changes multiple parameters including result ranking weights, filtering thresholds, and display priorities based on analyzed user activity data. By adjusting these parameters in real-time according to user behavior patterns, the system maintains comprehensive result coverage while optimizing the presentation to minimize user effort in finding relevant information
3Adaptability or versatility
If a search application uses broad search terms to retrieve information, then more potential results are obtained, but the results include many irrelevant items that do not match user interests
Solution Approach 1:
The system merges the original search term with identified user interests and contextual information from analyzed application data. By combining these multiple information sources into a unified search query, the system maintains the broad coverage of the original search term while automatically incorporating relevance filters that reflect user interests, thereby retrieving comprehensive yet personalized results
4Loss of information
If the system analyzes user activity data from multiple applications to identify interests, then search results can be more relevant to user interests, but the device complexity and computing resources required increase
Solution Approach 1:
The system employs self-service mechanisms where user activity data is automatically collected, analyzed, and processed without requiring manual configuration or intervention. Interest profiles are self-updated based on continuous monitoring of application usage patterns, and search result filtering is automatically adjusted based on identified interests, reducing the operational complexity despite the sophisticated analysis performed
Data Source
AI summary
For online searching, data of a user activity is analyzed where the user activity occurs at an application other than a search application and the search application is used for the online searching. In response to the analyzing, a topic of interest of the user is identified. A search term input is detected at the search application. A subject of the search term is identified. The search term is modified using a modifier. The modifier is a term related to the topic of interest, and the online searching occurs in response to the modified search term.