Event Suggestion System Using User Data Analysis
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Solution Overview
Problem
Users face challenges in finding relevant events and attractions while traveling to new locations, as existing methods require time-consuming keyword searches that often yield irrelevant results.
Innovation Solution
A computing device analyzes user data such as search history, social media profiles, and geographic location to predict interests and automatically suggest nearby events relevant to those interests, ranking them based on relevance for quick and efficient discovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a person spends time researching events and attractions using traditional search methods, then they may find some events, but the search process is time-consuming and may not turn up all relevant events or only general interest events
Solution Approach 1:
The system performs preliminary analysis of user data (search history, browsing history, electronic communications, social media profiles, geographic location history) to predict user interests before the user actually searches for events. This advance preparation allows the system to have event recommendations ready when the user needs them, eliminating the time-consuming search process while ensuring comprehensive and personalized results.
2Adaptability or versatility
If traditional search methods are used to find events, then some events may be found, but the results are not personalized and only show events of general public interest
Solution Approach 1:
The system uses user data from multiple sources (search history, browsing history, electronic communications, social media profiles, geographic location history) as feedback to continuously refine and predict user interests. This feedback mechanism enables the system to adapt to individual user preferences and provide personalized event recommendations without requiring users to manually specify their preferences, thereby maintaining ease of operation while achieving high personalization.
3Productivity
If a computing device continuously monitors and analyzes user data to predict interests and suggest events, then relevant events can be automatically suggested, but power consumption increases
Solution Approach 1:
The system performs data collection and interest prediction in advance, storing the results for later use. By pre-processing user data and predicting interests before they are needed for event suggestions, the system reduces the need for continuous real-time analysis during event discovery. This approach maintains high productivity in event recommendation while significantly reducing power consumption during actual usage.
Data Source
AI summary
A computing device may determine a geographic location of the computing device. The computing device may receive information associated with a user. The information may include a search history, a browsing history, an electronic communication message, a social media profile, and a geographic location history. The computing device may determine, based on the information associated with the user, a predicted interest of the user. The computing device may determine, based on the predicted interest of the user, events within a threshold distance from the geographic location of the computing device. The computing device may determine, based on a strength of a relationship between the predicted interest and respective event information associated with each of the events, a ranking of the events. The computing device may output, based on the ranking, at least a portion of the event information for at least one of the events.