This document presents an interest-based tour recommendation system for large-scale cultural heritage virtual environments. It addresses issues like information overload, navigation and time constraints. A recommendation model uses a point of interest's categories and a user's interests to provide personalized recommendations. A route planning algorithm then generates tours that maximize user interest while respecting time limits, including as many recommended points as possible in the routes. Evaluation shows the effect of time constraints on tour profit. The system aims to enhance virtual environments for cultural education through personalized recommendations and tour planning that considers user preferences and constraints.

1. Constrained Interest-based Tour
Recommendations in Large Scale
Cultural Heritage Virtual
Environments
Vasileios Komianos, PhD Candidate
Ionian University, Corfu, Greece
vkomianos@ionio.gr
and
Konstantinos Oikonomou
Ionian University, Corfu, Greece
okon@ionio.gr
Corfu, Greece, 6-8 July 2015

2. 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

3. Virtual Environments on Cultural Heritage
Applications
VE*: Computer generated and
simulated environment
enabling users to interact
with 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

4. The considered Virtual Environment
Characteristics:
• 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.

5. 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 information
Solution: 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

6. 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.

7. Recommendation model: How it works
Iu(POIm), interest profit provided to user by POIm
Iu(cx), user's interest on a category
k, number of categories
Iu(POIm) = ( I(cx) + I(cy) + ... + I(cz) ) × 1/k
Iu(POIm) = 0.275
Iu(POIn) = 0.5 → Iu(POIn) = 0.5
Iu(POIm) = 0.275
Recommendation model: How it works
descending order

8. 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

9. 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

10. The route planning algorithm
Step 1
→ Discover routes from start-point to end-point
Example:
• start-point: O5,
• end-point: O3,
• recommended POI: O1

11. The route planning algorithm
Step 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.

12. 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

13. Results
Relation between profit and cost of the discovered routes

14. Results
The effect of time constraint on the profit of the routes

15. Results
The effect of time constraint on the profit of the routes

16. 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.

17. Thank you for your attention!