Constrained Interest-base Tour Recommendations in Large Scale Cultural Heritage Virtual Environments...
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Transcript of Constrained Interest-base Tour Recommendations in Large Scale Cultural Heritage Virtual Environments...
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Constrained Interest-based Tour Recommendations in Large Scale Cultural Heritage Virtual EnvironmentsVasileios Komianos, PhD CandidateIonian University, Corfu, [email protected] OikonomouIonian University, Corfu, [email protected]
Corfu, Greece, 6-8 July 2015
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In this presentation
Large scale cultural heritage virtual environments:• characteristics, aims and prospects• related issues:
– wayfinding and navigation– information overload– time constraints– users' preferences and interests
• and the proposed solution approaches:– interest-based recommendations– time-constrained route planning
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Virtual Environments on Cultural Heritage Applications
VE*: Computer generated and simulated environment enabling users to interact w ith it.
Characteristics:● intuitiveness● interactivity● immediacy
Advantages:● easy of use● increases information intake
Based on:● 3D graphics● mental immersion● real time
Modern cultural heritage trends:● Globalization● Democratization● Use of ICT**
VEs are applicable on promotion, preservation and restoration of cultural heritage.
*Virtual Environment**Information & Communication
Technology
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The considered Virtual EnvironmentCharacteristics:• Large scale• Large number of points of interest• Complex structure• Various content categories: archaelogical sites, museums, castles,
etc.
Requirements:• Platforms: PCs / mobile devices• Virtual tours• support visiting of the real area.
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Related issues• Users' information overload:
– large number of entities of interest– large amount of relevant information– subjected to various categories– users interests– effort to handle the informationSolution: Recommendation model
• Tour planning:– large scale virtual environment – large number of points of interest– complex structure– user-centered constraints (interests, time, distance etc.)Solution: Route planning algorithm
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The proposed recommendation model
• Α Point of Interest (POI) either belongs to a category or not.
• POIs may belong to more than one categories.
• Users are interested in particular categories• A user's interest in a particular category Iu(c), 0 ≤
Iu(c) ≤ 1.
• Results: The POIs belonging to categories that users are interested in are recommended.
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Recommendation model: How it works
Iu(POIm), interest profit provided to user by POImIu(cx), user's interest on a categoryk, number of categories
Iu(POIm) = ( I(cx) + I(cy) + ... + I(cz) ) × 1/k
Iu(POIm) = 0.275Iu(POIn) = 0.5 → Iu(POIn) = 0.5
Iu(POIm) = 0.275
Recommendation model: How it works
descending order
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The Tour Planning Problem
Decision making on:• the sequence of POIs to be visited,• the paths to be traversed
Caused by:• users limited spatial knowledge• large scale• complex structure• user-centered constraints
Hard to solve problem Tour (route) planning algorithms are used
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The route planning algorithm
Aims:• Routes from start-point to end-point• Maximum profit I(rn) - according to user's interests• Not exceeding user's available time C(rn) ≤ T
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The route planning algorithmStep 1
→ Discover routes from start-point to end-point
Example: • start-point: O5, • end-point: O3, • recommended POI: O1
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The route planning algorithmStep 2→ Extend discovered routes to include recommended POIs*: → for each route
→ for each POI on the route → for each of the recommendations not included
→ discover and include paths to the recommended POI → indicate that this is to be visited → discover shortest path to continue the route
→ if the POI is recommended → indicate that this is to be visited
* Each route is extended till as many as possible recommended points are included, the routes exceeding user's time limit are discarded.
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The route planning algorithm Step 3
→ Terminate the algorithm when:– all the recommended points are included or – when all the new routes discovered exceed the time
limit→ Return the route having:
– maximum profit– minimum cost
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ResultsRelation between profit and cost of the discovered routes
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ResultsThe effect of time constraint on the profit of the routes
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ResultsThe effect of time constraint on the profit of the routes
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Conclusions• Virtual environments can be extended to overcome information
overload and tour planning issues and effectively serve for cultural and environmental education
• The presented recommendation model:– is able to provide personalized content recommendations– and it can be integrated into virtual environments to extend their
functionality• The presented route planning algorithm:
– provides tour recommendations according to the content recommendations and user-specific constraints
– and it can be extended to support:• learning or• guiding scenarios
• The considered virtual environment integrates the presented recommendation model and the route planning algorithm as well as navigation assistance methods to guide users.
• The considered virtual environment can be used as an on-site tour/educational guide when executed on mobile devices.
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Thank you for your attention!