Survey on Recommendation of Personalized Travel Sequence

Abstract

Now a day, traveling recommendation is important for user who is the plan for traveling. There are many existing techniques which are used for travel recommendation. In this paper explain a personalized travel sequence recommendation system using travelogues and users contributed photos with metadata of this photo by comparing existing different technique. It recommends personalized users travel interest and recommend a sequence of travel interest instead of an individual point of interest. The existing system cannot complete the requirement i.e. personalized and sequential recommendation together. To solve the problem of providing personalized and sequential travel package recommendation, a topical package model is created using social media data in which automatically mine user travel interest with another attribute like time, cost, and season of traveling. The proposed system uses the travelogues and photos of social media which map each user and routes description to the topical package area to induce user topical package model and route topical package model. To suggest personalized POI sequence, first famous routes are stratified as per the similarity between user package and route package. Then high stratified routes are more optimized by using social similar users travel records for more accuracy.

Parallel Semi‐supervised enhanced fuzzy Co‐Clustering (PSEFC) and Rapid Association Rule Mining (RARM) based frequent route mining algorithm for travel sequence recommendation on big social media

“…According to the mapping of the tags of user images, the user topical package model is studied to topical package space in this research. The individual user semantic info is linked by means of utilizing travel sequence and ontology is made to the entire user dependent upon the travel sequence . It encompasses user topical interest distribution ( α ( U ) ), user consumption capability ( β ( U ) ), preferred travel time distribution ( γ ( U ) ), and preferred travel season distribution ( δ ( U ) ).…”

Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity

“…So far, the travel recommendations given by apps and OTAs are classical routes with scenic spots ranked by the number of visitors or preferences of most travellers. us, the recommendations are not suitable for every visitor because of a lack of personalization [4]. When modelling and solving the tour route planning problem, most papers investigate user preference and give travel route recommendations with a fixed start and endpoints [5], not taking the spatiotemporal travel sequence, length of stay, and ways of transport into consideration [6,7].…”