Partition-Based Partial Personalized Model for Points of Interest Recommendations: A Review

Abstract: The rapid climb of online travel information imposes an increasing challenge for tourists who need to choose between an outsized numbers of travel packages to satisfy their personalized requirements. On the opposite side, to urge more business and profit, the travel companies need to understand these preferences from different tourists and serve more attractive packages. Therefore, the demand for intelligent travel services, from both tourists and travel companies, is predicted to extend dramatically. Since recommender systems are successfully applied to reinforce the standard of service for patrons during a number of fields its natural direction to develop recommender systems for personalized travel package recommendation. Our approach isn't only personalized to user's travel interest but also ready to recommend a travel sequence instead of individual Points of Interest (POIs). Topical package space including representative tags, the distributions of cost, visiting time and visiting season of every topic, is mined to bridge the vocabulary gap between user travel preference and travel routes. We map both user's and routes' textual descriptions to the topical package space to urge user topical package model and route topical package model (i.e., topical interest, cost, time and season).

Keywords: Travel recommendation, geo-tagged photos, social media, multimedia information retrieval, online interest.