Improvising Personalized Travel Recommendation System with Recency Effects

Improvising Personalized Travel Recommendation System with Recency Effects

IEEE BASE PAPER ABSTRACT:

A travel recommendation system based on social media activity provides a customized place of interest to accommodate user-specific needs and preferences. In general, the user’s inclination towards travel destinations is subject to change over time. In this project, we have analyzed users’ twitter data, as well as their friends and followers in a timely fashion to understand recent travel interest. A machine learning classifier identifies tweets relevant to travel. The travel tweets are then used to obtain personalized travel recommendations. Unlike most of the personalized recommendation systems, our proposed model takes into account a user’s most recent interest by incorporating time-sensitive recency weight into the model. Our proposed model has outperformed the existing personalized place of interest recommendation model, and the overall accuracy is 75.23%.

PROJECT OUTPUT VIDEO:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

• System : Pentium i3 Processor
• Hard Disk : 500 GB.
• Monitor : 15’’ LED
• Input Devices : Keyboard, Mouse
• Ram : 4 GB

SOFTWARE REQUIREMENTS: 

• Operating system : Windows 10/11.
• Coding Language : JAVA.
• Frontend : JSP, HTML, CSS, JavaScript.
• IDE Tool : Netbeans 8.2
• Database : MYSQL

REFERENCE:

Paromita Nitu, Joseph Coelho, and Praveen Madiraju, “Improvising Personalized Travel Recommendation System with Recency Effects”, IEEE, BIG DATA MINING AND ANALYTICS, 2021.

PROJECT COST: Rs.3000/

PROJECT BUY LINK: Click here to buy this project in Online.