Exhibition Booth Recommendation via User Behavior Similarity
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Solution Overview
Problem
Existing methods for recommending booths to visit in exhibitions and driving routes lack personalization, relying on static user parameters and failing to consider past behavior, leading to information overload and ineffective recommendations.
Innovation Solution
A computer-implemented method that accesses user and history databases to determine similarity in visiting behavior, recommending the next booth or passing point based on the user's past visits, current location, and preferences, allowing for dynamic and personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static user parameters are used for recommendations, then the recommendation system is simple to implement, but the personalization and effectiveness of recommendations deteriorates
Solution Approach 1:
The patent transforms the static recommendation system into a dynamic one by continuously updating user profiles based on real-time behavior data. The system adapts recommendations as users interact with exhibited items, transitioning from fixed parameters to fluid, evolving user models that reflect current interests and behavior patterns.
Solution Approach 2:
The system implements feedback loops where user interactions with recommended items are tracked and fed back into the recommendation engine. This creates a continuous improvement cycle where recommendations are refined based on actual user behavior, enhancing personalization while maintaining system simplicity through automated learning.
2Measurement precision
If past behavior data is collected and analyzed, then recommendation accuracy improves, but information processing complexity and storage requirements increase
Solution Approach 1:
The patent extracts only the most relevant behavioral features from user interaction data, focusing on key patterns such as item selection, time spent viewing, and navigation paths. By selecting only critical data points rather than processing all possible behaviors, the system maintains high accuracy while reducing computational complexity.
Solution Approach 2:
The system transforms raw behavioral data into simplified numerical parameters and metrics that capture essential user preferences. By converting complex behavior patterns into standardized parameters, the patent reduces data processing complexity while preserving the information needed for accurate recommendations.
3Measurement precision
If comprehensive user behavior tracking is implemented, then recommendation quality improves, but user privacy concerns and data security requirements worsen
Solution Approach 1:
The patent employs ephemeral, anonymized data representations that are processed and then discarded or transformed. User behavior data is converted into aggregated statistical patterns rather than being stored as identifiable individual records, allowing quality recommendations while minimizing privacy risks through deliberate data ephemerality.
Solution Approach 2:
The system introduces anonymization and aggregation as intermediary processing steps between data collection and recommendation generation. These intermediaries transform identifiable user data into anonymous behavioral patterns, serving as a buffer that protects privacy while preserving the analytical value needed for quality recommendations.
4Productivity
If real-time recommendations are provided, then user experience and timeliness improve, but computational resource consumption increases
Solution Approach 1:
The patent pre-computes and stores recommendation patterns, user profiles, and item characteristics during off-peak periods. This preliminary preparation allows the system to deliver real-time recommendations by retrieving and combining pre-processed data rather than performing complex computations at the moment of user interaction, reducing real-time resource consumption.
Solution Approach 2:
The recommendation system is divided into independent modular components that can process different aspects of user behavior separately. This segmentation allows parallel processing and efficient resource allocation, enabling real-time performance while managing computational resources through distributed, specialized processing units.
Data Source
AI summary
Disclosed is a computer-implemented method for recommending booths-to-visit to a user. The method includes: accessing a map database of an exhibition; accessing a history database that stores a plurality of records indicative of booths visited by a previous visitor in the exhibition; accessing a user record that includes data indicative of the booths which the user has visited; determining similarity level between each record in the history database and the user record, selecting one of the records according to the determined similarity level so as for the selected record to function as a reference record, usable to determine the booths not yet visited by the user; obtaining the user's current location in the exhibition, and determining a target booth, from the booths not yet visited by the user, by referring to the current location and the map database; and sending to the user a message indicative of the target booth.