期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2017
卷号：6
期号：12
页码：1921-1924
出版社：Shri Pannalal Research Institute of Technolgy
摘要：Recommendation systems and adaptive systems have been introduced in travel application to sustain the travelers in their administrative process. Travel based suggestion and journey planning are demanding tasks because of various importance preferences and trip limitations such as restriction of time, source and destination points for each tourist. Large amount of data can be composed from the Internet and travel guides, but these assets normally suggested personalized Point of Interest (POI) that is considered to be common, but the existing studies do not provide satisfactory information to the substance preference of the users or hold to their constraint. Unlike most existing travel recommendation approaches, the recommendation approach is not only personalized to user’s travel interest but also able to suggest a travel sequence rather than personalized Points of Interest (POIs). Topical package space together with representative tag, the distributions of cost, visiting time and visiting season of each topic, is mined to connection the terms gap between user travel preference and travel routes. The proposed studies take advantage of the balancing of two kinds of social media; travelogue and community-contributed photograph. The proposed system map both user’s and routes’ textual description to the topical package space to get client/user topical package model and route topical package representation (i.e., source, destination, topical interest, cost, time and season). To suggest personalized POI sequence, first, well-known routes are rank according to the resemblance between user package and route package. Then top ranked routes are additional optimized by community similar users’ travel records. In addition, the massive volume of information makes it a challenge for every tourist to pay notice to a potential set of POIs to make a visit in any unknown city. To sort out these problems, this research work expand this method to provide an author topic matrix modeling algorithm (ATMMA) is suggested for personalize tour. Hence, this method is highly explained here for tour recommendation problem based on similar user and similar city forecast, which considers user tags.
关键词：Travel recommendation; photo collection; social media; information retrieval.
