Constrained Interest-based Tour Recommendations in Large Scale Cultural Heritage Virtual EnvironmentsVasileios Komianos, PhD CandidateIonian University, Corfu, Greecevkomianos@ionio.grandKonstantinos OikonomouIonian University, Corfu, Greeceokon@ionio.gr

Corfu, Greece, 6-8 July 2015

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

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

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.

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

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.

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

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

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

The route planning algorithmStep 1

→ Discover routes from start-point to end-point

Example: • start-point: O5, • end-point: O3, • recommended POI: O1

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.

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

ResultsRelation between profit and cost of the discovered routes

ResultsThe effect of time constraint on the profit of the routes

ResultsThe effect of time constraint on the profit of the routes

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.

Thank you for your attention!