The document outlines a collaborative recommendation system designed to help tourists explore and visit nature-oriented and mythical places in Nepal. It discusses methodologies such as collaborative filtering, nearest-neighbor algorithms, and matrix factorization to enhance user experience by suggesting personalized travel options. Challenges include data collection limitations and processing times, while future enhancements aim to improve user engagement through better information and faster services.

1. Paryatak Sahayatri
Abhibandu Kafle
Chandan Gupta Bhagat
Sanjeev Kumar Pandit
Sudha Bhandari

2. Which places should I Visit
I would like to visit a place with nature. Places where I feel
like I am in heaven.
I want to be away from the work and experience a lot of
adventure, trekking and hiking.
Let’s go to explore the mythical places. History and myths
are what fascinates us.

3. Solution
Collaborative: "Tell me what's
popular among my peers"

4. Beginning
• A recommender system
• Suggests different places to the tourists
• Uses the characteristics and history of the users/tourists

5. Purpose/Objectives
• Self Agent
• Information before visit
• Explore hidden treasures/places of Nepal
• Promote tourism

6. Challenges
• List the intended outcomes for this training session.
• Each objective should be concise, should contain a verb, and
should have a measurable result.
• Tip: Click and scroll in the notes pane below to see examples,
or to add your own speaker notes.

7. System Architecture

8. Methodology
•Tools and Technologies Used
• Algorithms

9. Tools & Technologies Used

10. Algorithm
•Collaborative Filtering
▫ Nearest-Neighbor
▫ Association Rules
▫ Matrix Factorization Model

11. Collaborative Filtering
• Widely-used recommendation approach
• Prediction the utility of items for a user
▫ Matrix Factorization Model
▫ Association rules
▫ Nearest-Neighbor

12. Nearest-Neighbor
• Memory-based approach
• Utilizes the entire user-item
• Approach includes
▫ User-based methods
▫ Item-based methods

13. Association rules
• Each transaction for association rule mining is the set of
items bought by a particular user.
•We can find item association rules, e.g.,
visit_X, visit_Y -> visit_Z

14. Matrix Factorization Model
• Map both users and items
ȓ푢푖= 푞푖
푇푝푢 (1)
• 푞푖 & 푝푢 are the vectors of item and users ȓ푢푖 is rating item ‘i‘
• Factor vectors (푝푢 and 푞푖 ), minimizes the regularized squared
error on the set of known ratings:
푚푖푛
푞∗, 푝∗
(푢,푖)∈푘
(푟푢푖 − 푞푖
푇푝푢)2+휆(||푞푖 ||2 + ||푝푢||2) (2)

15. Implementation
• Data Collection
• Implementing Recommendation
• Stakeholder Analysis
• Market For Recommendation System

16. Data Collection
• Source:
▫ Nepal Tourism Board
▫ Ministry of Tourism, Culture and Civil Aviation

17. Implementing Recommendation
• New Users
▫ Those who haven’t visited any place in Nepal
▫ Based on their characteristics: Nationality, Age Group &
Gender
• Existing Users
▫ Those who have already visited some places in Nepal
▫ Based on their history of visiting places

18. Stakeholder Analysis
• Identifying all the stakeholders
• Prioritizing Stakeholders
• Understanding Stakeholders
• Stakeholders Involvement

19. Market For Recommendation System
• Establish/Maintain the communication with the
customers/users
• Business Model

20. Limitations
• Time to process recommendation is comparatively high
• Focused only on foreign tourists
• Lack of complete information about the places

21. Future Enhancements
• Make the service for Nepalese
• Faster data processing
• Complete information about every tourist place in
Nepal
• Tourist service recommendation
• Path to the destination

22. Future Enhancements contd..
• User generated content and social networking services
•Multiple days tour planning
• Intelligent UI

23. Output : Home Page (Guest User)

24. Output : Home Page (Registered User) - Recommendation

25. Output : Search

26. Output : Age-Sex Based Visualization

27. Output : Nationality based Visualization