User Profile: Theoretical Background

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2020 by IJETT Journal
Volume-68 Issue-8
Year of Publication : 2020
Authors : Mohamed Grida, Lamiaa Fayed, Mohamed Hassan
DOI :  10.14445/22315381/IJETT-V68I8P203S


MLA Style: Mohamed Grida, Lamiaa Fayed, Mohamed Hassan  "User Profile: Theoretical Background" International Journal of Engineering Trends and Technology 68.8(2020):10-17. 

APA Style:Mohamed Grida, Lamiaa Fayed, Mohamed Hassan. User Profile: Theoretical Background  International Journal of Engineering Trends and Technology, 68(8),10-17.

The user profile is commonly used nowadays to support personalization, web search, adaptation and any other user-based features applications including recommendation systems. User profiles are a data structure that is used to store user’s characteristics and preferences. Therefore, it has a significant impact on the recommendation accuracy. However, it contains more sensitive information about the user, such as demographic characteristics and physical location that reveal privacy.
There are different models for user profile representation in the literature. Each has its advantages and disadvantages. The most frequently used models are reviewed in this paper. Moreover, this paper investigates the most widely used approaches that handle privacy issues in the user profiling.


[1] D. Kim, C. Park, J. Oh, S. Lee, and H. Yu, “Convolutional Matrix Factorization for Document Context-Aware Recommendation,” pp. 233–240, 2016.
[2] L. Zheng, V. Noroozi, and P. S. Yu, “Joint Deep Modeling of Users and Items Using Reviews for Recommendation,” WSDM, pp. 425–433, 2017.
[3] O. Abdillah and M. Adriani, “Mining User Interests through Internet Review Forum for Building Recommendation System,” Int. Conf. Adv. Inf. Netw. Appl. Work., pp. 564– 569, 2015.
[4] W. S. Yang, H. C. Cheng, and J. Ben Dia, “A location-aware recommender system for mobile shopping environments,” Expert Syst. Appl., vol. 34, no. 1, pp. 437–445, 2008.
[5] B. Fang, S. Liao, and K. Xu, “A novel mobile recommender system for indoor shopping,” Expert Syst. Appl., vol. 39, no. 15, pp. 11992–12000, 2012.
[6] J. Borràs, A. Moreno, and A. Valls, “Intelligent tourism recommender systems : a survey,” Expert Syst. Appl., 2014.
[7] K. L. Skillen, L. Chen, C. D. Nugent, M. P. Donnelly, W. Burns, and I. Solheim, “Ontological user profile modeling for context-aware application personalization,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7656 LNCS, pp. 261–268, 2012.
[8] C. L. Huang, P. H. Yeh, C. W. Lin, and D. C. Wu, “Utilizing user tag-based interests in recommender systems for social resource sharing websites,” Knowledge-Based Syst., vol. 56, pp. 86–96, 2014.
[9] Z. Sun, L. Han, W. Huang, X. Wang, and X. Zeng, “Recommender systems based on social networks,” J. Syst. Softw., vol. 99, pp. 109–119, 2015.
[10] B. Barragans Martinez, E. Costa Montenegro, and J. Juncal Martinez, “Developing a recommender system in a consumer electronic device,” Expert Syst. Appl., vol. 42, no. 9, pp. 4216–4228, 2015.
[11] B. Lika, K. Kolomvatsos, and S. Hadjiefthymiades, “Lika2014.pdf.” 2014.
[12] Z. Xu, C. Chen, T. Lukasiewicz, Y. Miao, and X. Meng, “Tag-Aware Personalized Recommendation Using a Deep- Semantic Similarity Model with Negative Sampling,” Proc. 25th ACM Int. Conf. Inf. Knowl. Manag. - CIKM ’16, pp. 1921–1924, 2016.
[13] C. L. Huang, P. H. Yeh, C. W. Lin, and D. C. Wu, “Utilizing user tag-based interests in recommender systems for social resource sharing websites,” Knowledge-Based Syst., vol. 56, pp. 86–96, 2014.
[14] H. Kawashima, T. Matsushita, and S. Satake, “PORSCHE : A Physical Objects Recommender System for Cell Phone Users,” 2006.
[15] Y. Blanco-Fernandez, M. Lopez-Nores, J. J. Pazos-Arias, and J. Garcia-Duque, “An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles,” Eng. Appl. Artif. Intell., vol. 24, no. 8, pp. 1385–1397, 2011.
[16] E. Costa-Montenegro, A. B. Barragns-Martnez, and M. Rey- Lpez, “Which App? A recommender system of applications in markets: Implementation of the service for monitoring users’ interaction,” Expert Syst. Appl., vol. 39, no. 10, pp. 9367– 9375, 2012.
[17] 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.
[18] J. Diederich and T. Iofciu, “Finding communities of practice from user profiles based on folksonomies,” CEUR Workshop Proc., vol. 213, pp. 288–297, 2006.
[19] C. S. Lengsfeld and R. A. Shoureshi, “User-Profile based Web Page Recommendation System and User Profile based Web Page Recommendation Method,” vol. 1, no. 19, 2008.
[20] P. Bhattacharyya, A. Garg, and S. F. Wu, “Analysis of user keyword similarity in online social networks,” Soc. Netw. Anal. Min., vol. 1, no. 3, pp. 143–158, 2011.
[21] T. Ruotsalo, K. Haav, and A. Stoyanov, “SMARTMUSEUM: A mobile recommender system for the Web of Data,” J. Web Semant., vol. 20, pp. 50–67, 2013.
[22] a Sieg, A. Sieg, B. Mobasher, B. Mobasher, R. Burke, and R. Burke, “Web search personalization with ontological user pro les,” CIKM ’07 Proc. Sixt. ACM Conf. Conf. Inf. Knowl. Manag., pp. 525–534, 2007.
[23] W. Wang and K. Lin, “Ontology-based User Profile Model Used in Information Retrieval,” J. Comput. Inf. Syst. v5 i3, vol. 3, pp. 1613–1621, 2009.
[24] M. Shmueli-Scheuer, H. Roitman, D. Carmel, Y. Mass, and D. Konopnicki, “Extracting user profiles from large scale data,” Proc. 2010 Work. Massive Data Anal. Cloud MDAC 10, pp. 1–6, 2010.
[25] J. K. Kim and Y. H. Cho, “Using Web Usage Mining and SVD to Improve E-commerce Recommendation Quality,” 6th Pacific Rim Int. Work. Multi-Agents, PRIMA 2003, Seoul, Korea, Novemb. 7-8, 2003. Proc., no. 1, pp. 86–97, 2003.
[26] W. S. Yang and S. Y. Hwang, “ITravel: A recommender system in mobile peer-to-peer environment,” J. Syst. Softw., vol. 86, no. 1, pp. 12–20, 2013.
[27] V. Eyharabide and A. Amandi, “Ontology-based user profile learning,” Appl. Intell., vol. 36, no. 4, pp. 857–869, 2012.
[28] D. Heckmann, T. Schwartz, B. Brandherm, M. Schmitz, and M. von Wilamowitz-Moellendorff, “GUMO - the General User Model Ontology,” Proc. 10th Int. Conf. User Model., pp. 428–432, 2005.
[29] M. Golemati, A. Katifori, C. Vassilakis, G. Lepouras, and C. Halatsis, “Creating an Ontology for the User Profile: Method and Applications,” Proc. 1st Int. Conf. Res. Challenges Inf. Sci. (RCIS 2007), pp. 407–412, 2007.
[30] K. R. Ananthapadmanaban and S. K. Srivatsa, “Personalization of user Profile: Creating user Profile Ontology for Tamilnadu Tourism,” Int. J. Comput. Appl., vol. 23, no. 8, pp. 975–8887, 2011.
[31] W. Liu, F. Jin, and X. Zhang, “Ontology-based user modeling for E-commerce system,” 2008 3rd Int. Conf. Pervasive Comput. Appl. ICPCA08, vol. 1, pp. 260–263, 2008.
[32] P. Ladyzynski and P. Grzegorzewski, “Vague preferences in recommender systems,” Expert Systems with Applications, vol. 42, no. 24. pp. 9402–9411, 2015.
[33] N. D. Rodríguez, M. P. Cuéllar, J. Lilius, and M. D. Calvoflores, “Knowledge-Based Systems A fuzzy ontology for semantic modelling and recognition of human behaviour,” 2014.
[34] H. Yin, B. Cui, L. Chen, Z. Hu, and C. Zhang, "Modeling Location-Based User Rating Profiles for Personalized Recommendation", vol. 9, no. 3. 2015.
[35] J. Liu, P. Dolan, and E. R. Pedersen, “Personalized News Recommendation Based on Click Behavior,” Proc. 15th Int. Conf. Intell. user interfaces, pp. 31–40, 2010.
[36] A. Rodriguez-carrion, D. Rebollo-monedero, J. Forné, and C. Campo, “Entropy-Based Privacy against Profiling of User Mobility,” Entropy, pp. 3913–3946, 2015.
[37] H. Zisopoulos, S. Karagiannidis, and S. Antaris, “Content- Based Recommendation Systems,” no. November, 2008.
[38] V. Toubiana, D. Boneh, and H. Nissenbaum, “Adnostic : Privacy Preserving Targeted Advertising,” Proc. Netw. Distrib. Syst. Symp., pp. 1–23, 2010.
[39] M. Fredrikson and B. Livshits, “R E P RIV : Re-Envisioning In-Browser Privacy,” Proc. IEEE Symp. Secur. Priv., 2010.
[40] S. Puglisi, J. Parra-arnau, J. Forné, and D. Rebollomonedero, “Computer Standards & Interfaces On contentbased recommendation and user privacy in social-tagging systems,” Comput. Stand. Interfaces, vol. 41, pp. 17–27, 2015.
[41] J. Parra-Arnau, D. Rebollo-Monedero, and J. Forné, “Measuring the privacy of user profiles in personalized information systems,” Futur. Gener. Comput. Syst., vol. 33, no. 2014, pp. 53–63, 2014.
[42] I. F. Akyildiz and W. Wang, “The predictive user mobility profile framework for wireless multimedia networks,” IEEE/ACM Trans. Netw., vol. 12, no. 6, pp. 1021–1035, 2004.
[43] M. A. Bayir, M. Demirbas, and N. Eagle, “Discovering SpatioTemporal mobility profiles of cellphone users,” in 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks and Workshops, 2009.
[44] G. Loseto, M. Ruta, F. Scioscia, E. Di Sciascio, and M. Mongiello, “Mining the user profile from a smartphone: A multimodal agent framework,” in CEUR Workshop Proceedings, 2013, vol. 1099, pp. 66–72.
[45] L. Hella and J. Krogstie, “A profile ontology for personalised mobile shopping support,” CEUR Workshop Proc., vol. 585, pp. 13–24, 2010.
[46] K.-L. Skillen, C. Liming, and N. Chris D, “A user profile ontology based approach for assisting people with dementia in mobile environments.,” Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., vol. 34, pp. 6390–6393, 2012.
[47] J. Morse and J. Grubb, “Prefetching Content Based On A Mobile User Profile,” patent, vol. 1, no. 19, 2008.
[48] W. Paireekreng and K. W. Wong, “Mobile content personalisation using intelligent user profile approach,” 3rd Int. Conf. Knowl. Discov. Data Mining, WKDD 2010, pp. 241–244, 2010.
[49] D. Gupta and N. Chavhan, “Ontological user profiling for adaptive re-ranking in mobile web search,” 2014 Int. Conf. Converg. Technol. I2CT 2014, pp. 1–5, 2014.
[50] M. Iwata, T. Hara, K. Shimatani, and T. Mashita, “A location-based content search system considering situations of mobile users,” Procedia Comput. Sci., vol. 5, pp. 426–433, 2011.
[51] K. Mouratidis and M. L. Yiu, “Anonymous Query Processing in Road Networks,” IEEE Trans. Knowl. Data Eng., pp. 1– 14, 2010.
[52] P. L. W. Peng, T. W. W. Ku, and J. X. J. A. Hamilton, “A Cloaking Algorithm based on Spatial Networks for Location Privacy,” pp. 90–97, 2008.
[53] C. Piao and X. Li, “Privacy Preserving-Based Recommendation Service Model of Mobile Commerce and Anonimity Algorithm,” 2015 IEEE 12th Int. Conf. E-bus. Eng., pp. 420–427, 2015.
[54] M. Mano and Y. Ishikawa, “Anonymizing user location and profile information for privacy-aware mobile services,” Int. Work. Locat. Based Soc. Networks - LBSN ’10, p. 68, 2010.
[55] H. Shin, V. Atluri, and J. Vaidya, “A profile anonymization model for privacy in a personalized location based service environment,” IEEE Int. Conf. Mob. Data Manag., pp. 73– 80, 2008.
[56] I. Armaç and D. Evers, “Client side personalization of smart environments,” Proc. - Int. Conf. Softw. Eng., pp. 57–59, 2008.
[57] S. Ceri, P. Dolog, M. Matera, and W. Nejdl, “Model-driven design of web applications with client-side adaptation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3140, pp. 201–214, 2004.
[58] M. Richardson, “Predictive Client-side Profiles for Personalized Advertising,” KDD’11, 2011.
[59] S. Gerber, M. Fry, J. Kay, B. Kummerfeld, G. Pink, and R. Wasinger, “PersonisJ : Mobile , Client-Side User Modelling,” Int. Conf. User Model. Adapt. Pers., pp. 111– 122, 2010.
[60] V. Coroama and M. Langheinrich, “Personalized Vehicle Insurance Rates,” Ubiquitous Comput. Work. Privacy- Enhanced Pers., pp. 1–4, 2000.
[61] G. Beigi, R. Guo, A. Nou, Y. Zhang, and H. Liu, “Protecting User Privacy : An Approach for Untraceable Web Browsing History and Unambiguous User Profiles,” WSDM ’19, pp. 213–221, 2019.
[62] H. Polat and W. Du, “SVD-based collaborative filtering with privacy,” Proc. ACM Symp. Appl. Comput., vol. 1, pp. 791– 795, 2005.
[63] S. Berkvosky, Y. Eytani, T. Kuflik, and F. Ricci, “Enhancing privacy and preserving accuracy of a distributed collaborative filtering,” RecSys’07 Proc. 2007 ACM Conf. Recomm. Syst., pp. 97–104, 2007.
[64] A. Machanavajjhala, A. Korolova, and A. Das Sarma, “Personalized social recommendations accurate or private?,” Proc. VLDB Endow., vol. 4, no. 7, pp. 440–450, 2011.

user profile, Level Of Interest, Privacy, Recommender System