Abstract
Konuma dayalı sosyal ağlar, son on yılda,
kullanıcının konum geçmişlerine dayanarak tercihlerini araştırmamız için bize
yeni bir platform sağlayarak önemli ölçüde gelişti. Konuma dayalı sosyal
ağların çoğu, kullanıcıların varlıklarını açıklayabilecekleri,
yorumlayabilecekleri veya ipucu bırakabilecekleri bir kategori hiyerarşisi
altına yerleştirilen çeşitli mekanlar sağlar. Coğrafi bilgili konum önerileri
birçok araştırmacının ilgisini çekmesine rağmen, araştırma projelerinin çoğunda
zamanın kullanıcının tercihleri üzerindeki etkisi göz ardı edilmiştir. Bir
kullanıcı, günün farklı saatlerinde ziyaret etmek için farklı mekanları tercih
edebileceğinden, belirli bir kategoride aynı miktarda giriş yapan iki
kullanıcı, o mekanda bulunma zamanına bağlı olarak daha az benzer olabilir. Ayrıca,
geleneksel işbirliğine dayalı filtreleme teknikleri, tüm kullanıcıların
tercihlerini göz önünde bulundururken, yalnızca kategori uzmanlarının
tercihlerini göz önünde bulundurarak, o kategorideki bir mekanı önermek, öneri
performansını daha da artırabilir. Bu amaçla, mekânları önermek için
ölçeklenebilir zamana dayalı yeni bir uzman destekli işbirliğine dayalı
filtreleme yaklaşımı önerilmiştir. Bu yeni yaklaşımda öncelikle, belirli bir
kategoride yapılan giriş kayıtlarının çeşitliliği göz önünde bulundurularak
kategorilere göre kullanıcıların uzmanlığı araştırılır ve her kategori için
uzmanların en üst m tanesi seçilir. Daha sonra, her bir kullanıcı-mekan çifti
için tercih puanı, günün zaman aralığı ile ilgili olarak hesaplanır. Giriş
kayıt saati bilgilerini farklı aşamalarda dikkate alan üç algoritma
geliştirilmiştir. İlk algoritma, önceden tanımlanmış her bir zaman aralığı için
kullanıcılar arasındaki benzerlik değerlerini hesaplar. İkinci algoritma, bu
belirli bir zaman aralığında kullanıcı-mekan giriş frekans matrisini dikkate
alır. Üçüncü algoritma hem benzerlik değerlerinden hem de zaman aralığının
kullanıcı-mekan giriş frekansı matrisinden faydalanır. Son olarak, en üst-k
sıradaki konumlar kullanıcıya öneri olarak sunulur. Önerilen algoritmalar iki
büyük ölçekli Foursquare veri kümesi ile değerlendirilmiş ve temel
yaklaşımlarla karşılaştırılmıştır.
References
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Abstract
References
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- [2] Jie Bao, Yu Zheng and Mohamed F. Mokbel. 2012. Location based and preference-aware recommendation using sparse geo-social networking data. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems, 199–208. ACM
- [3] Yu Zheng, Xing Xie and Wei-Ying Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th International Conference on World Wide Web - WWW 09.
- [4] Xin Cao, Gao Cong and Christian Søndergaard Jensen. 2010. Mining significant semantic locations from gps data. Proceedings of the VLDB Endowment 3, 1-2, 1009–1020.
- [5] Kazuki Kodama, Yuichi Iijima, Xi Guo and Yoshiharu Ishikawa. 2009. Skyline queries based on user locations and preferences for making location-based recommendations. In Proceedings of the 2009 International Workshop on Location Based Social Networks, 9-16. ACM.
- [6] Moon-Hee Park, Jin-Hyuk Hong and Sung-Bae Cho. 2007. Location-based recommendation system using bayesian user’s preference model in mobile devices. In International Conference on Ubiquitous Intelligence and Computing 1130-1139.
- [7] Anastasios Noulas, Salvatore Scellato, Neal Lathia and Cecilia Mascolo. 2012. A random walk around the city: New venue recommendation in location-based social networks. In International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing (socialcom).
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- [10] Betim Berjani and Thorsten Strufe. 2011. A recommendation system for spots in location-based online social networks. In Proceedings of the 4th Workshop on Social Network Systems. ACM.
- [11] Josh Jia-Ching Ying, Eric Hsueh-Chan Lu, Wen-Ning Kuo and Vincent S. Tseng. 2012. Urban point-of-interest recommendation by mining user check-in behaviors. In Proceedings of the ACM SIGKDD International Workshop on Urban Computing 63-70. ACM.
- [12] Xian Wu, Yuxiao Dong, Baoxu Shi, Ananthram Swami and Nitesh V. Chawla. 2018. Who will Attend This Event Together? Event Attendance Prediction via Deep LSTM Networks. In Proceedings of the 2018 SIAM International Conference on Data Mining 180-188. Society for Industrial and Applied Mathematics.
- [13] Mohamed Sarwat , Justin J. Levandoski, Ahmed Eldawy and Mohamed F. Mokbel. 2014. LARS*: An efficient and scalable location-aware recommender system. IEEE Transactions on Knowledge and Data Engineering, 26, 6, 1384-1399.
- [14] Chi-Yin Chow, Jie Bao and Mohamed F. Mokbel. 2010. Towards locationbased social networking services. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks 31–38. ACM.
- [15] Pavlos Kosmides, Chara Remoundou, Konstantionos Demestichas, Ioannis Loumiotis, Evgenia Adamopoulou and Michael Theologou. 2014. A location recommender system for location-based social networks. In Proceedings of International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) 277-280. IEEE. [16] Yifan Hu, Yehuda Koren and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In ICDM 263–272.
- [17] Dingqi Yang, Daqing Zhang, Zhiyong Yu, Zhiwen Yu. 2013. Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs. Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp 13 479-488. ACM.
- [18] Dingqi Yang ; Daqing Zhang ; Vincent W. Zheng ; Zhiyong Yu. 2015. Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Transactions on Systems, Man, and Cybernetics: Systems.
- [19] Mao Ye1, Peifeng Yin, Wang-Chien Lee and Dik-Lun Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information - SIGIR 11, 325–334.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Makaleler(Araştırma) |
| Authors | |
| Publication Date | April 13, 2020 |
| Published in Issue | Year 2020 Volume: 13 Issue: 1 |
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