近年來，隨著行動通信技術的進步，帶動了帶動適地性服務與應用的迅速發展。在眾多的適地性服務當中，旅遊行程推薦是其中一項非常熱門的議題。雖然已經有不少的研究探討旅遊行程推薦議題，但多數的研究只考慮利用景點的組合來規劃符合使用者限制的旅遊行程。在另一方面，也有一部分的研究是討論遊套裝的行程推薦，旅遊套裝相對來說擁有花費較少而且便利性較高的優勢。然而，目前沒有研究探討同時整合景點與旅遊套裝行程來做旅遊推薦。事實上，這種混合式推薦系統可以提供使用者更多益處，但卻也存在著許多挑戰，例如在此種即時應用上之效率問題等。本研究提出一個創新的架構，名為Package-Attraction-based Trip Recommender (PATR)。此架構能夠有效地結合景點與旅遊套裝行程來推薦滿足多項使用者限制的個人化旅遊行程。在PATR中，我們首先根據使用者的偏好以及時間的特性，提出了一個自動化與個人化的景點與旅遊套裝行程的評分推論模組。接著，為了有效地找出最佳旅遊行程並滿足多項使用者限制以及同時考慮景點以及旅遊套裝行程，我們提出一個高效率演算法，名為Hybrid Trip-Mine。此外，為了改善演算法執行效率以及記憶體使用量，我們提出了分數估計與分數邊界繃緊兩種改良策略。據我們所知，本研究是第一個針對旅遊行程推薦同時考慮景點與旅遊套裝行程之研究。透過Gowalla網站所蒐集到的真實簽到資料，我們進行了一系列完整的實驗，實驗結果顯示PATR不論是在準確度以及運算效能上都有相當優異的表現。

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.

[1] Facebook, http://www.facebook.com/
[2] Foursquare, http://foursquare.com
[3] Gowalla, http://gowalla.com
[4] Lonely Planet, http://www.lonelyplanet.com/
[5] TripAdvisor, http://www.tripadvisor.com/
[6] Yahoo Travel, http://travel.yahoo.com/
[7] J. Bao, Y. Zheng and M. F. Mokbel, "Location-Based and Preference-Aware Recommendation Using Sparse Geo-Social Networking Data," in ACM GIS, pp. 199-208, 2012.
[8] B. Berjani and T. Strufe, "A Recommendation System for Spots in Location-Based Online Social Network," in SNS, 2011.
[9] H. Chen, M. S. Arefin and Y. Morimoto, "Place Recommendation Based on Users Check-in History for Location-Based Services," in IJNC, pp. 228-243, 2013.
[10] Z. Cheng, J. Caverlee, K. Lee, and D. Sui, "Exploring Millions of Footprints in Location Sharing Services," in ICWSM, 2011.
[11] E. Cho, S. A. Myers, and J. Leskovec, "Friendship and Mobility: User Movement in Location-Based Social Networks," in KDD, pp. 1082-1090, 2011.
[12] Y. Ge, Q. Liu, H. Xiong, A. Tuzhilin and J. Chen, "Cost-Aware Travel Tour Recommendation," in KDD, pp. 983-991, 2011.
[13] T. Horozov, N. Narasimhan and V. Vasudevan, "Using Location for Personalized POI Recommendations in Mobile Environments," in SAINT, pp. 124-129, 2006.
[14] H.-P. Hsieh and C.-T. Li, "Constructing Routes with User Preference from Location Check-in Data," in ACM UbiComp, pp. 195-198, 2013.
[15] H.-P. Hsieh, C.-T. Li and S.-D. Lin, "Exploiting Large-Scale Check-in Data to Recommend Time-Sensitive Routes," in ACM UrbComp, pp. 55-62, 2012.
[16] Y. Huang and L. Bian, "A Bayesian Network and Analytic Hierarchy Process Based Personalized Recommendations for Tourist Attractions over the Internet," ESWA, 36(1), pp. 933-943, 2009.
[17] K. Järvelin and J. Kekäläinen, "Cumulated Gain-Based Evaluation of IR Techniques," ACM TOIS, 20(4), pp. 422-446, 2002.
[18] J. Kim, H. Kim and J.-H. Ryu, "TripTip: A Trip Planning Service with Tag-based Recommendation," in CHI, pp. 3467-3472, 2009.
[19] Q. Liu, Y. Ge, Z. Li, E. Chen and H. Xiong, "Personalized Travel Package Recommendation," in ICDM, pp. 407-416, 2011.
[20] X. Liu, Y. Liu and K. Aberer, "Personalized Point-of-Interest Recommendation by Mining Users’ Preference Transition," in ACM CIKM, pp. 733-738 , 2013.
[21] E. H.-C. Lu, C.-Y. Chen and V. S. Tseng, "Personalized Trip Recommendation with Multiple Constraints by Mining User Check-in Behaviors," in ACM GIS, pp. 209-218, 2012.
[22] E. H.-C. Lu, C.-Y. Lin, and V. S. Tseng, "Trip-Mine: An Efficient Trip Planning Approach with Travel Time Constraints," in MDM, pp. 152-161, 2011.
[23] A. Noulas, S. Scellato, R. Lambiotte, M. Pontil, and C. Mascolo, " A Tale of Many Cities: Universal Patterns in Human Urban Mobility," in PLoS 1 7(5), 2012.
[24] A. Noulas, S. Scellato, C. Mascolo, and M. Pontil, " An Empirical Study of Geographic User Activity Patterns in Foursquare," in ICWSM, 2011.
[25] S. M. Rahimi and X. Wang, "Location Recommendation Based on Periodicity of Human Activities and Location Categories, " PAKDD 2013, Part II, LNAI 7819, pp. 377–389, 2013.
[26] S. Scellato and C. Mascolo, "Measuring User Activity on an Online Location-Based Social Network, " in Proceedings of international Workshop on Network Science for Communication Networks , 2011.
[27] S. Scellato, A. Noulas and C. Mascolo, "Exploiting Place Features in Link Prediction on Location-Based Social Networks," in ACM SIGKDD, pp. 1046-1054, 2011.
[28] P. Vansteenwegen, W. Souffriau, G.V. Berghe and D.V. Oudheusden, "The City Trip Planner: An Expert System for Tourists," ESWA, 38(6), pp. 6540–6546, 2011.
[29] X. Xiao, Y. Zheng, Q. Luo and X. Xie, "Finding Similar Users Using Category-Based Location History,” in ACM SIGSPATIAL, pp. 442-445, 2010.
[30] M. Xie, L. V.S. Lakshmanan and P. T. Wood, "Breaking out of the Box of Recommendations: From Items to Packages," in ACM RecSys, pp.151–158, 2010.
[31] M. Ye, Y. Peifeng, and W.-C. Lee, "Location Recommendation for Location-Based Social Networks," in SIGSPATIAL GIS, pp. 458–461, 2010.
[32] M. Ye, P. Yin, W.-C. Lee and D.-L. Lee, "Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation," in SIGIR, pp. 325-334, 2011.
[33] H. Yin, C. Wang, N. Yu and L. Zhang, "Trip Mining and Recommendation from Geo-tagged Photos," in ICMEW, pp. 540-545, 2012.
[34] Q. Yuan, G. Cong, Z. Ma, A. Sun and N. Magnenat-Thalmann, "Time-Aware Point-of-Interest Recommendation," in SIGIR, pp. 363-372, 2013.
[35] Y. Zheng and X. Xie, "Learning Travel Recommendations from User Generated GPS Traces," in ACM TIST, 2(1), 2011.
[36] Y. Zheng, X. Xie and W. Y. Ma, "GeoLife: A Collaborative Social Networking Service among User Location and Trajectory," in IEEE Data Eng. Bull. 33(2), pp. 32-39, 2010.
[37] V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang, "Collaborative Location and Activity Recommendations with GPS History Data," in WWW, pp. 1029-1038, 2010.
[38] Y. Zheng, L. Z. Zhuang, X. Xie and W. Y. Ma, "Recommending Friends and Locations Based on Individual Location History," in ACM TWEB, pp. 1-44, 2011.