Improvising Personalized Travel Recommendation System with Recency Effects

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%.

Personalized travel route recommendation using collaborative filtering based on GPS trajectories

Travelling is a critical component of daily life.With new technology,personalized travel route recommendations are possible and have become a new research area.A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations,based on the road networks and users’travel preferences.In this paper,we define users’travel behaviours from their historical Global Positioning System(GPS)trajectories and propose two personalized travel route recommendation methods–collaborative travel route recommendation(CTRR)and an extended version of CTRR(CTRR+).Both methods consider users’personal travel preferences based on their historical GPS trajectories.In this paper,we first estimate users’travel behaviour frequencies by using collaborative filtering technique.A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model.The CTRR+method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability.This paper also conducts some case studies based on a real GPS trajectory data set from Beijing,China.The experimental results show that the proposed CTRR and CTRR+methods achieve better results for travel route recommendations compared with the shortest distance path method.