Prediction of Sequencial Travel Route Recommendation for Journey Planning

Large amount of data can be collected from the Internet and travel guides, but these resources normally recommend personalized Point of Interest (POI) that is considered to be familiar, but they do not provide sufficient information to the interest preference of the users or hold to their trip constraints. The resources collected from the Internet and travel guides, normally recommend familiarized Point of Interest (POI). Such resources do not provide sufficient information to the users interest preference. Compared to the existing approaches, this approach is both personalized and also able to recommend a travel sequence. Topical package space is constructed which includes representative tags, the cost distributions, visiting time and visiting season of each topic. These resources are mined to bridge the vocabulary gap between user travel preference and travel routes. It utilizes two kinds of social media: travelogue and community-contributed photos. The textual descriptions of both user and routes are mapped to the topical package space to get user topical package model and route topical package model. First famous routes are ranked according to the similarity between user package and route package. Then top ranked routes are further optimized by social similar users travel records. The method suggests that the POIs are optimized to the users’ interest preferences and POI popularity. Index Terms Point of Interest, Topical package space, user topical package , route topical package travelogue, community-contributed photos