Socio-user Context Aware-Based Recommender System: Context Suggestions for A Better Tourism Recommendation
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Keywords:
Context suggestions, recommender system, social context-based, tourism, user
Abstract
The existing tourism recommender system model is mostly predictive analytics for destination recommendations (item recommendation). Limited research has been conducted in the discussion of a recommender system model, particularly context suggestion. Thus, it is necessary to develop a recommender system model not only to predict tourism destinations but also to suggest contexts appropriate for tourist preferences (context suggestions). A deep learning method was used to create a model of the socio-user context aware-based recommender system for context suggestions. The attribute used as a label to suggest context was uHijos, uCuisine, uAmbience, and uTransport. The accuracy of the socio-user context aware-based recommender system in suggesting the context of uHijos, uAmbience, and uTransport was 100% with an error rate of 0%. It was found that only the level of recognition of the model in suggesting uCuisine was less accurate (below 30%) with a classification error for more than 70%. Performance evaluation of the socio-user model context-based recommender system was considered efficient, particularly for the evaluation of the level of accuracy, completeness (recall/sensitivity), precision, and a harmonic average of precision and recall (F-score), mainly for label/context of uHijos, uAmbience, and uTransport.
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References
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[4] B. McKercher, “Towards a taxonomy of tourism products,” Tourism Management, vol. 54, pp. 196–208, Jun. 2016, doi: 10.1016/j.tourman.2015.11.008.
[5] S. Khusro, Z. Ali, and I. Ullah, “Recommender Systems: Issues, Challenges, and Research Opportunities,” in Lecture Notes in Electrical Engineering 376, 2016, pp. 1179–1189. doi: 10.1007/978-981-10-0557-2_112.
[6] J. Illig, A. Hotho, R. Jäschke, and G. Stumme, “A Comparison of Content-Based Tag Recommendations in Folksonomy Systems,” in Knowledge Processing and Data Analysis, 2011, pp. 136–149. doi: 10.1007/978-3-642-22140-8_9.
[7] A. B. Barragáns-Martíneza, E. Costa-Montenegroa, J. C. Burguilloa, M. Rey-Lópezb, F. A. Mikic-Fontea, and A. Peleteiroa, “A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition,” Information Sciences, vol. 180, no. 22, pp. 4290–4311, 2010.
[8] A. Bellogín, P. Castells, and I. Cantador, “Improving memory-based collaborative filtering by neighbour selection based on user preference overlap,” in OAIR ’13 Proceedings of the 10th Conference on Open Research Areas in Information Retrieval, 2013, pp. 145–148.
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[10] T. M. Chang and W. F. Hsiao, “Model-based collaborative filtering to handle data reliability and ordinal data scale,” in Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on Shanghai, 2011, pp. 2065–2069. doi: 10.1109/FSKD.2011.6019879.
[11] Y. Bergner, S. Droschler, G. Kortemeyer, S. Rayyan, D. Seaton, and D. E. Pritchard, “Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response Theory,” 2012.
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[17] C. C. Aggarwal, “Social and Trust-Centric Recommender Systems,” in Recommender Systems, Cham: Springer International Publishing, 2016, pp. 345–384. doi: 10.1007/978-3-319-29659-3_11.
[18] D. Buhalis and A. Amaranggana, “Smart Tourism Destinations Enhancing Tourism Experience Through Personalisation of Services,” in Information and Communication Technologies in Tourism 2015, I. Tussyadiah and A. Inversini, Eds. Switzerland: Springer International Publishing, 2015, pp. 377–389. doi: 10.1007/978-3-319-14343-9.
[19] M. Andrejevic and M. Burdon, “Defining the Sensor Society,” Television & New Media, vol. 16, no. 1, pp. 19–36, Jan. 2015, doi: 10.1177/1527476414541552.
[20] P. P. Tallon, “Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost,” Computer, vol. 46, no. 6, pp. 32–38, Jun. 2013, doi: 10.1109/MC.2013.155.
[21] G. Chen and D. Kotz, “A Survey of Context-Aware Mobile Computing Research,” Science, vol. 3755, pp. 1–16, 2000, doi: 10.1.1.117.4330.
[22] B. Schilit, N. Adams, and R. Want, “Context-aware computing applications,” in Workshop on Mobile Computing Systems and Applications, 1994, pp. 85–90. doi: 10.1109/MCSA.1994.512740.
[23] G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, and P. Steggles, “Towards a Better Understanding of Context and Context-Awareness,” in Computing Systems, vol. 40, no. 3, 1999, pp. 304–307. doi: 10.1007/3-540-48157-5_29.
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[25] P. Gundecha and H. Liu, “Mining Social Media: A Brief Introduction,” in 2012 TutORials in Operations Research, INFORMS, 2012, pp. 1–17. doi: 10.1287/educ.1120.0105.
[26] M. A. Russell, “Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More,” in Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites, O’Reilly Media, Inc., 2014, pp. 45–88.
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[28] M. A. Russell, Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites. O’Reilly Media, Inc., 2014. doi: 10.1081/E-ELIS3-120043522.
[29] Z. Sun et al., “Recommender systems based on social networks,” Journal of Systems and Software, vol. 99, pp. 109–119, Jan. 2015, doi: 10.1016/j.jss.2014.09.019.
[30] C. Biancalana, F. Gasparetti, A. Micarelli, and G. Sansonetti, “An approach to social recommendation for context-aware mobile services,” ACM Transactions on Intelligent Systems and Technology, vol. 4, no. 1, pp. 1–31, 2013, doi: 10.1145/2414425.2414435.
[31] J. Hong, E.-H. Suh, J. Kim, and S. Kim, “Context-aware system for proactive personalized service based on context history,” Expert Systems with Applications, vol. 36, no. 4, pp. 7448–7457, May 2009, doi: 10.1016/j.eswa.2008.09.002.
[32] M.-H. Kuo, L.-C. Chen, and C.-W. Liang, “Building and evaluating a location-based service recommendation system with a preference adjustment mechanism,” Expert Systems with Applications, vol. 36, no. 2, pp. 3543–3554, Mar. 2009, doi: 10.1016/j.eswa.2008.02.014.
[33] H. Gao, J. Tang, X. Hu, and H. Liu, “Exploring temporal effects for location recommendation on location-based social networks,” in Proceedings of the 7th ACM conference on Recommender systems - RecSys ’13, 2013, pp. 93–100. doi: 10.1145/2507157.2507182.
[34] G. Chen and L. Chen, “Augmenting service recommender systems by incorporating contextual opinions from user reviews,” User Modeling and User-Adapted Interaction, vol. 25, no. 3, p. 295, 2015.
[35] H. Wu, K. Yue, X. Liu, Y. Pei, and B. Li, “Context-Aware Recommendation via Graph-Based Contextual Modeling and Postfiltering,” International Journal of Distributed Sensor Networks, vol. 11, no. 8, p. 613612, Aug. 2015, doi: 10.1155/2015/613612.
[36] Z. Xu, L. Chen, and G. Chen, “Topic based context-aware travel recommendation method exploiting geotagged photos,” Neurocomputing, vol. 155, pp. 99–107, May 2015, doi: 10.1016/j.neucom.2014.12.043.
[37] D. Yang, D. Zhang, Z. Yu, and Z. Wang, “A sentiment-enhanced personalized location recommendation system,” in Proceedings of the 24th ACM Conference on Hypertext and Social Media - HT ’13, 2013, pp. 119–128. doi: 10.1145/2481492.2481505.
[38] J.-D. Zhang and C.-Y. Chow, “CoRe: Exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations,” Information Sciences, vol. 293, pp. 163–181, Feb. 2015, doi: 10.1016/j.ins.2014.09.014.
[39] T. H. Dao, S. R. Jeong, and H. Ahn, “A novel recommendation model of location-based advertising: Context-Aware Collaborative Filtering using GA approach,” Expert Systems with Applications, vol. 39, no. 3, pp. 3731–3739, Feb. 2012, doi: 10.1016/j.eswa.2011.09.070.
[40] O. Khalid, M. U. S. Khan, S. U. Khan, and A. Y. Zomaya, “OmniSuggest: A Ubiquitous Cloud-Based Context-Aware Recommendation System for Mobile Social Networks,” IEEE Transactions on Services Computing, vol. 7, no. 3, pp. 401–414, Jul. 2014, doi: 10.1109/TSC.2013.53.
[41] M. A. Domingues, A. M. Jorge, and C. Soares, “Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems,” Information Processing & Management, vol. 49, no. 3, pp. 698–720, May 2013, doi: 10.1016/j.ipm.2012.07.009.
[42] L. Hong, L. Zou, C. Zeng, L. Zhang, J. Wang, and J. Tian, “Context-Aware Recommendation Using Role-Based Trust Network,” ACM Transactions on Knowledge Discovery from Data, vol. 10, no. 2, pp. 1–25, Oct. 2015, doi: 10.1145/2751562.
[43] X. Ren, M. Song, H. E, and J. Song, “Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation,” Neurocomputing, vol. 241, pp. 38–55, Jun. 2017, doi: 10.1016/j.neucom.2017.02.005.
[44] X. Ramirez-Garcia and M. García-Valdez, “Post-Filtering for a Restaurant Context-Aware Recommender System,” in Studies in Computational Intelligence, 2014, pp. 695–707. doi: 10.1007/978-3-319-05170-3_49.
[45] S. Valencia Rodríguez and H. L. Viktor, “A Personalized Location Aware Multi-Criteria Recommender System Based on Context-Aware User Preference Models,” in IFIP Advances in Information and Communication Technology, 2013, pp. 30–39. doi: 10.1007/978-3-642-41142-7_4.
[46] N. M. Villegas, C. Sánchez, J. Díaz-Cely, and G. Tamura, “Characterizing context-aware recommender systems: A systematic literature review,” Knowledge-Based Systems, vol. 140, pp. 173–200, Jan. 2018, doi: 10.1016/j.knosys.2017.11.003.
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Published
2023-12-25
How to Cite
Kusuma Adi Achmad, Lukito Edi Nugroho, Achmad Djunaedi, & Widyawan. (2023). Socio-user Context Aware-Based Recommender System: Context Suggestions for A Better Tourism Recommendation. International Journal on Information and Communication Technology (IJoICT), 9(2), 96-119. https://doi.org/10.21108/ijoict.v9i2.858
Section
Data Science
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