Integrating Point-of-Interests and Travel Packages for Personalized Trip Recommendation with Multiple User Constraints

• Main Authors: Shih-HsinFang, 方詩欣
• Other Authors: Shin-Mu Tseng
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/72641829420864532944

碩士 === 國立成功大學 === 資訊工程學系 === 102 === With the advances of mobile communication techniques in recent years, numerous kinds of Location-Based Services (LBSs) have been developed and one popular application of LBSs is trip recommendation. Although there exist already a number of studies on this topic in literatures, most of them focused on combining a set of point-of-interests (POIs, or say attractions) as a trip based on user-specific constraints. In another way, some few works discussed making recommendation in terms of travel packages, which have the benefits of lower cost and higher convenience. However, no prior work explores to integrate attractions and travel packages simultaneously for trip recommendation. In fact, such a hybrid-style recommender can provide higher benefits for users although there exist critical challenges here like the efficiency issue in such kind of real-time applications. In this work, we propose a novel framework named Package-Attraction-based Trip Recommender (PATR) to efficiently recommend the personalized trips satisfying multiple constraints by effectively combining attractions and packages. In PATR, a Score Inference Model is proposed to infer the scores of attractions and packages by taking user-based preference and temporal-based properties into account. Then, the Hybrid Trip-Mine algorithm is proposed to efficiently discover the optimal trip which satisfies the multiple user-specific constraints with both of attractions and packages considered simultaneously. Furthermore, we propose two pruning strategies based on Hybrid Trip-Mine, named Score Estimation (SE) and Score Bound Tightening (SBT), to further improve the execution efficiency and memory utilization. To the best of our knowledge, this is the first work on travel recommendation that considers attractions and packages simultaneously. Through extensive experimental evaluations, our proposed approaches were shown to deliver excellent performance.