Predictive Search Suggestions Using Location and Query Logs
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Solution Overview
Problem
Existing search engines struggle to provide location-specific search results and suggestions that cater to individual user interests, failing to leverage user profiles, social graphs, and query logs to enhance relevance.
Innovation Solution
A system that determines search results and query suggestions based on user location, utilizing user profiles, social graph data, and query logs to provide location-specific and interest-based suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search engines provide general search results without location-specific customization, then the system complexity remains low, but the relevance of search results to local user interests deteriorates
Solution Approach 1:
The system pre-determines search results associated with locations in proximity to the user's current location before the user actually submits a search query. By using the user profile and location data to proactively generate and store location-specific search results in advance, the system reduces the complexity of real-time processing while improving the relevance and speed of result delivery when the user searches.
Solution Approach 2:
The search results are segmented and organized by geographic location and user profile characteristics. The system divides the vast database of search results into location-specific subsets and user-interest-specific subsets, allowing for more efficient retrieval and presentation of relevant results without requiring the entire system to be restructured for each query.
2Reliability
If search engines use user profiles, social graphs, and query logs to personalize results, then the relevance to individual users improves, but the data processing complexity and computational resources worsen
Solution Approach 1:
The system performs preliminary processing of user profiles, social graph data, and query logs to pre-determine search results before actual search queries are submitted. By analyzing and organizing this multi-source data in advance, the system creates pre-computed result sets that can be quickly retrieved and presented, reducing the computational burden during actual search operations while maintaining high personalization accuracy.
3Ease of operation
If search engines provide location-aware suggestions before user input, then the user experience and relevance improve, but the amount of data to be processed and transmitted worsens
Solution Approach 1:
The system extracts and presents only the most relevant location-specific search results and query suggestions to the user, rather than transmitting all possible data. By filtering and selecting the most pertinent information based on user profile and location data, the system improves user experience with personalized suggestions while minimizing the volume of data that needs to be processed and transmitted.
Data Source
AI summary
A method of generating search suggestions includes receiving an indication of a current location of the user device. After a user accesses a search function on the user device and before the user submits a search request, the method includes determining search results associated with locations in proximity to the current location of the user device, using query log data indicating selections of past search results by users after presentation of the past search results in response to respective past queries. The method also includes determining relative positioning of the search results based on the number of users that selected each search result, determining categories associated with the search results, grouping the search results by the determined categories; and sending the grouped search results and the one or more categories associated with the search results to the user device for display according to the determined relative positioning.