Visitor Recommendation Engine for Personalized Non-Profit Venue Routes
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Solution Overview
Problem
Non-profit venues like museums and zoos struggle to provide personalized experiences to visitors, failing to engage their philanthropic interests and interests in specific exhibits, leading to a lack of engagement and fundraising opportunities.
Innovation Solution
Implementing a system that uses location tracking, interaction analysis, and recommendation engines to personalize visitor experiences through personalized multimedia content, route suggestions, and campaign participation opportunities based on visitor interests and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional approaches (printed placards, audio-visual guides, mobile applications) are used to convey information to visitors, then information can be provided to visitors about various sections or specific objects, but the information is generic and fails to provide a personalized experience based on visitor interests
Solution Approach 1:
The system performs preliminary actions by collecting visitor preference data, interaction data, and demographic information before the visitor arrives at the venue. This pre-processing of visitor data enables the recommendation engine to generate personalized recommendations upon the visitor's arrival, resolving the contradiction by preparing personalization capabilities in advance without requiring complex real-time processing during the visit.
Solution Approach 2:
The patent introduces a recommendation engine as an intermediary component that processes visitor data and generates personalized recommendations. This intermediary layer sits between the visitor and the venue information systems, translating generic information into personalized content based on visitor profiles, thereby enabling personalization without directly modifying the underlying information delivery infrastructure.
2Productivity
If conventional approaches are used, then information delivery is simple and straightforward, but visitor engagement and philanthropic participation are not effectively encouraged
Solution Approach 1:
The system implements feedback mechanisms by continuously collecting visitor interaction data, tracking visitor movements and engagements with exhibits, and using this feedback to refine and update visitor profiles. This feedback loop enables the recommendation engine to improve personalization accuracy over time and dynamically adjust recommendations, thereby increasing visitor engagement and philanthropic participation while systematically capturing visitor interest data.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting recommendation parameters based on visitor behavior patterns, interaction intensity, and contextual factors. The system modifies recommendation parameters such as relevance thresholds, timing of interventions, and types of information presented, thereby optimizing visitor engagement and fundraising effectiveness while adapting to individual visitor preferences.
3Productivity
If personalized information is provided to visitors based on their interactions and interests, then visitor engagement and philanthropic activity are enhanced, but tracking and analyzing visitor interactions requires complex systems
Solution Approach 1:
The patent applies universality by designing a multi-functional tracking system that simultaneously performs multiple functions: collecting location data, tracking interactions with exhibits, gathering demographic information, and analyzing visitor preferences. This universal system serves multiple purposes within a single integrated framework, reducing the need for separate complex systems for each function while enabling comprehensive personalization and engagement tracking.
Data Source
AI summary
A method for providing personalized non-profit venue visit recommendations to a visitor at a non-profit venue, comprising: providing a management interface for management of a set of multimedia assets; receiving a mapping of multimedia assets to a display in at least one site plan; receiving metadata for the set of multimedia assets; providing an interface for receiving data indicating personal interests of a visitor, including visitor-provided preference data and passively-collected visitor interaction data; receiving personal interest data for a plurality of non-profit venue visitors; receiving preference data relating to a prospective visitor; applying a machine learning system to analyze the metadata, the data indicating personal interests of the visitor, the personal interest data for the plurality of non-profit venue visitors, and the preference data relating to the prospective visitor; and generating a selection or sequence of non-profit venue location recommendations for the prospective visitor.