Abhibandu Kafle

Chandan Gupta Bhagat

Sanjeev Kumar Pandit

Sudha Bhandari

Paryatak Sahayatri

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.

Solution

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

•A recommender system

• Suggests different places to the tourists

•Uses the characteristics and history of the users/tourists

Beginning

• Self Agent

• Information before visit

• Explore hidden treasures/places of Nepal

• Promote tourism

Purpose/Objectives

• 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.

Challenges

System Architecture

Methodology

•Tools and Technologies Used

•Algorithms

Tools & Technologies Used

•Collaborative Filtering▫Nearest-Neighbor

▫ Association Rules

▫Matrix Factorization Model

Algorithm

•Widely-used recommendation approach

• Prediction the utility of items for a user▫ Matrix Factorization Model

▫ Association rules

▫ Nearest-Neighbor

Collaborative Filtering

•Memory-based approach

•Utilizes the entire user-item

•Approach includes▫ User-based methods

▫ Item-based methods

Nearest-Neighbor

• 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

Association rules

• Map both users and items ȓ𝑢𝑖= 𝑞𝑖

𝑇𝑝𝑢 (1)

• 𝑞𝑖 & 𝑝𝑢 are the vectors of item and users ȓ𝑢𝑖 is rating item ‘i‘

• Factor vectors (𝑝𝑢 and 𝑞𝑖), minimizes the regularized squared error on the set of known ratings: 𝑚𝑖𝑛𝑞∗, 𝑝∗

(𝑢,𝑖)∈𝑘

(𝑟𝑢𝑖 − 𝑞𝑖𝑇𝑝𝑢)

2+𝜆(||𝑞𝑖||2 + ||𝑝𝑢||

2) (2)

Matrix Factorization Model

• Data Collection

• Implementing Recommendation

• Stakeholder Analysis

• Market For Recommendation System

Implementation

• Source:▫ Nepal Tourism Board

▫ Ministry of Tourism, Culture and Civil Aviation

Data Collection

•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

Implementing Recommendation

• Identifying all the stakeholders

• Prioritizing Stakeholders

•Understanding Stakeholders

• Stakeholders Involvement

Stakeholder Analysis

• Establish/Maintain the communication with the customers/users

• Business Model

Market For Recommendation System

• Time to process recommendation is comparatively high

• Focused only on foreign tourists

• Lack of complete information about the places

Limitations

•Make the service for Nepalese

• Faster data processing

• Complete information about every tourist place in Nepal

• Tourist service recommendation

• Path to the destination

Future Enhancements

•User generated content and social networking services

•Multiple days tour planning

• Intelligent UI

Future Enhancements contd..

Output : Home Page (Guest User)

Output : Home Page (Registered User) - Recommendation

Output : Search

Output : Age-Sex Based Visualization

Output : Nationality based Visualization