References(37)
[1]
F. Ricci, L. Rokach, and B. Shapira, Introduction to recommender systems handbook, in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, eds. Boston, MA, USA, 2011, pp. 1-35.
[2]
T. Zhou, Z. Kuscsik, J. G. Liu, M. Medo, J. R. Wakeling, and Y. C. Zhang, Solving the apparent diversity-accuracy dilemma of recommender systems, Proc. Natl. Acad. Sci. USA, vol. 107, no. 10, pp. 4511-4515, 2010.
[4]
J. Coelho, P. Nitu, and P. Madiraju, A personalized travel recommendation system using social media analysis, in Proc.2018 IEEE Int Congress on Big Data (Big Data Congress), San Francisco, CA, USA, 2018.
[5]
L. A. Caldito, F. Dimanche, and S. Ilkevich, Tourist behaviour and trends, in Tourism in Russia: A Management Handbook, F. Dimanche and L. A. Caldito, eds. Bingley, UK: Emerald Group Publishing Limited, 2015, pp. 1-30.
[6]
A. J. N. Nzeko’O, M. Tchuente, and M. Latapy, Time weight content-based extensions of temporal graphs for personalized recommendation, in WEBIST 2017-Proc. 13th Int. Conf. Information Systems and Technologies, Porto, Portugal, 2017, pp. 268-275.
[7]
A. Gediminas, T. Alexander, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734-749, 2005.
[8]
J. L. Herlocker, J. A. Konstan, and J. Riedl, Explaining collaborative filtering recommendations, in Proc. ACM Conf. Computer Supported Cooperative Work, Philadelphia, PA, USA, 2000, pp. 241-250.
[9]
A. H. Celdrán, M. G. Pérez, F. J. García Clemente, and G. M. Pérez, Design of a recommender system based on users’ behavior and collaborative location and tracking, J. Comput. Sci., vol. 12, pp. 83-94, 2016.
[10]
Y. Ar, E. Bostanci, A genetic algorithm solution to the collaborative filtering problem, Expert Syst. Appl., vol. 61, pp. 122-128, 2016.
[11]
H. Koohi, K. Kiani, User based collaborative filtering using fuzzy C-means, Measurement, vol. 91, pp. 134-139, 2016.
[12]
J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, Recommender systems survey, Knowl.-Based Syst., vol. 46, pp. 109-132, 2013.
[13]
R. Burke, Hybrid recommender systems for electronic commerce, User Model. User-Adapt. Interact., vol. 12, no. 4, pp. 331-370, 2000.
[14]
K. Kulkarni, K. Wagh, S. Badgujar, and J. Patil, A study of recommender systems with hybrid collaborative filtering, Int. Res. J. Eng. Technol., vol. 3, no. 4, pp. 2216-2219, 2016.
[15]
R. Nugroho, J. Yang, Y. L. Zhong, C. Paris, and S. Nepal, Deriving topics in twitter by exploiting tweet interactions, in Proc. 2015 IEEE Int. Congress Big Data, New York, NY, USA, 2015, pp. 87-94.
[16]
P. Resnick and R. Sami, The influence limiter: Provably manipulation-resistant recommender systems, in RecSys’07 Proc. 2007 ACM Conf. Recommender System, Minneapolis, MN, USA, 2007, pp. 25-32.
[17]
D. Kotkov, S. Q. Wang, and J. Veijalainen, A survey of serendipity in recommender systems, Knowl.-Based Syst., vol. 111, pp. 180-192, 2016.
[18]
F. G. Zhang, Improving recommendation lists through neighbor diversification, presented at 2009 IEEE Int. Conf. Intelligent Computing and Intelligent Systems, Shanghai, China, 2009, pp. 222-225.
[19]
M. D. Ekstrand, P. Kannan, J. A. Stemper, J. T. Butler, J. A. Konstan, and J. T. Riedl, Automatically building research reading lists, in RecSys’10-Proc. 4th ACM Conf. Recommender Systems, Barcelona, Spain, 2010, pp. 159-166.
[20]
B. P. Knijnenburg, M. C. Willemsen, Z. Gantner, H. Soncu, and C. Newell, Explaining the user experience of recommender systems, User Model. User-adapt. Interact., vol. 22, nos. 4&5, pp. 441-504, 2012.
[21]
S. Dooms, Dynamic generation of personalized hybrid recommender systems, in RecSys 2013-Proc. 7th ACM Conf. Recommender Systems, New York, NY, USA, 2013, pp. 443-446.
[22]
S. G. Deng, L. T. Huang, G. D. Xu, X. D. Wu, and Z. H. Wu, On deep learning for trust-aware recommendations in social networks, IEEE Trans. Neural Networks Learn. Syst., vol. 28, no. 5, pp. 1164-1177, 2017.
[23]
G. B. Guo, J. Zhang, and N. Yorke-Smith, A novel recommendation model regularized with user trust and item ratings, IEEE Trans. Knowl. Data Eng., vol. 28, no. 7, pp. 1607-1620, 2016.
[24]
H. Z. Yin, B. Cui, L. Chen, Z. T. Hu, and C. Q. Zhang, Modeling location-based user rating profiles for personalized recommendation, ACM Trans. Knowl. Discov. Data, vol. 9, no. 3, p.19, 2015.
[25]
S. Van Canneyt, O. Van Laere, S. Schockaert, and B. Dhoedt, Using social media to find places of interest: A case study, in GEOCROWD 2012-Proc. 1st ACM SIGSPATIAL Int. Workshop on Crowdsourced Volunteered Geographic Information, Redondo Beach, CA, USA, 2012, pp. 2-8.
[26]
M. Xie, H. Z. Yin, H. Wang, F. J. Xu, W. T. Chen, and S. Wang, Learning graph-based poi embedding for location-based recommendation, in CIKM’16: Proc. 25th ACM Int. Conf. Information and Knowledge Management, Indianapolis, IN, USA, 2016, pp. 15-24.
[28]
A. Majid, L. Chen, G. Chen, H. T. Mirza, I. Hussain, and J. Woodward, A context-aware personalized travel recommendation system based on geotagged social media data mining, Int. J. Geogr. Inf. Sci., vol. 27, no. 4, pp. 662-684, 2013.
[29]
I. Guy, N. Zwerdling, I. Ronen, D. Carmel, and E. Uziel, Social media recommendation based on people and tags, in SIGIR 2010 Proc.-33rd Annu. Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., no. Lc, Geneva, Switzerland, 2010, pp. 194-201.
[30]
M. Parul, K. Daleep, Career trends and worldwide employment growth in tourism, travel and hospitality industry, An Int. Multidiscip. Res. J., vol. 3, no. 10, pp. 104-119, 2013.
[31]
M. Pennacchiotti and S. Gurumurthy, Investigating topic models for social media user recommendation, in Proc. 20th Int. Conf. Companion World Wide Web, Hyderabad, India, 2011, pp. 101-102.
[32]
D. Ajantha, J. Vijay, and R. Sridhar, A user-location vector based approach for personalised tourism and travel recommendation, in Proc. 2017 Int. Conf. Big Data Anal. Comput. Intell. ICBDACI 2017, Chirala, India, 2017, pp. 440-446.
[35]
Y. Chang, A. L. Dong, P. Kolari, R. Q. Zhang, Y. Inagaki, F. Diaz, H. Y. Zha, and Y. Liu, Improving recency ranking using twitter data, ACM Trans. Intell. Syst. Technol., vol. 4, no. 1, p. 4, 2013.
[36]
A. L. Dong, R. Q. Zhang, P. Kolari, J. Bai, F. Diaz, Y. Chang, Z. H. Zheng, and H. Y. Zha, Time is of the essence: Improving recency ranking using Twitter data, in Proc. 19th Int. Conf. World Wide Web, Raleigh, NC, USA, 2010, pp. 331-340, 2010.
[37]
A. Perrin, Social media usage: 2005-2015, in Pew Internet & American Life Project, Washington, DC, USA, 2015, pp. 2005-2015.