نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی مالی، دانشکده اقتصاد، مدیریت و حسابداری، دانشگاه یزد، یزد، ایران.

2 گروه مالی و حسابداری، دانشکده مدیریت و اقتصاد، دانشگاه یزد، یزد، ایران.

10.22105/imos.2021.290478.1117

چکیده

هدف: توسعه و پیشرفت علم و فن‌آوری، روزانه موجب ایجاد حجم زیادی از داده­های ساخت‌یافته، نیمه ساختاریافته و بدون ساختار با سرعتی بسیار سریع از منابع مختلف گردیده که منجر به اشتراک گذاشتن الگوهای مشترک بسیاری شده به‌نحوی‌که می­توان با استفاده از سیستم‌های توصیه کننده که بر اساس نیازهای کاربران طراحی شده با فیلتر و تجزیه‌وتحلیل این داده­هایشان، توصیه­هایی مربوط به محصول، کالا یا خدمات مورد علاقه آن‌ها ارائه داد. یکی از مسائل مهم در سیستم‌های توصیه کننده ارائه توصیه‌های شخصی‌سازی شده متناسب با روحیات کاربران است.
روش‌شناسی پژوهش: در این پژوهش با رویکرد نگاشت دانش با استفاده از کتابخانه bibliometrix در نرم‌افزار R به‌مرور کلیه پژوهش‌های انجام‌شده در خصوص کاربرد سیستم­های توصیه کننده در شخصی­سازی پرداخته می­شود.




یافته ها: در این پژوهش با استفاده از روش نگاشت دانش ضمن تعریف سیستم­های توصیه کننده و انواع آن، به معرفی نمای کلی از حیطه شخصی­سازی پرداخته و انواع مختلف شخصی­سازی ارائه می­شود. همچنین در مورد روند شخصی­سازی بحث نموده و در خصوص سیستم­های توصیه کننده به‌عنوان بخش جدایی‌ناپذیر از این فرایند نیز توضیحاتی مطرح شده است. در ادامه چالش­هایی که برای پیاده­سازی سیستم­های توصیه کننده وجود دارد ارائه شده است و نهایتاً حوزه­هایی که بحث شخصی­سازی سیستم­های توصیه کننده می­تواند در آن مطرح شود، 

کلیدواژه‌ها

عنوان مقاله [English]

Typology of Personalization in Recommender Systems

نویسندگان [English]

  • Marziyeh Nourahmadi 1
  • Hojjatollah Sadeqi 2

1 Department of Financial Engineering, Faculty of Economic, Management and Accounting, Yazd University, Yazd, Iran.

2 Department of Finance and Accounting, Faculty of Management and Economics, Yazd University, Yazd, Iran.

چکیده [English]

Purpose: With the development of science and technology, large volumes of structured, semi-structured, and unstructured data are generated daily at breakneck speeds from various sources. This data generated by different users share many common patterns that can be filtered and analyzed to make recommendations for a product, goods3, or service of interest to users. Recommender systems are software tools that are used to provide suggestions to users based on their needs. One of the critical issues in recommender systems is providing personalized advice that fits the users' mood.
Methodology: In this research, with the bibliometrics approach using bibliometrix library in R software, all the researches done on the application of recommended systems in personalization is reviewed.
Findings: In this research, using the bibliometrics approach, while defining recommender systems and their types, an overview of the field of personalization is introduced, and different types of personalization are presented. It also discusses the process of personalization and describes recommender systems as an integral part of this process. The following are the challenges that exist for implementing recommendation systems, and finally, the areas in which the issue of personalization of recommendation systems can be raised.




Originality/Value: The results of this study with a comprehensive review of all research in this field can help researchers in ideation and selection of appropriate methods in classifying and analyzing data as a toolbox for the use of recommender systems in personalization.

کلیدواژه‌ها [English]

  • Personalization
  • Recommender systems
  • user personality
  • Bibliometrics Approach
Adomavicius, G., & Tuzhilin, A. (2001). Using data mining methods to build customer profiles. Computer34(2), 74-82. DOI: 10.1109/2.901170
Adomavicius, G., & Tuzhilin, A. (2002). An architecture of e-Butler: a consumer-centric online personalization system. International journal of computational intelligence and applications2(03), 313-327. https://doi.org/10.1142/S1469026802000658
Adomavicius, G., & Tuzhilin, A. (2005 a). Personalization technologies: a process-oriented perspective. Communications of the ACM48(10), 83-90. https://doi.org/10.1145/1089107.1089109
Adomavicius, G., & Tuzhilin, A. (2005b). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering17(6), 734-749. DOI: 10.1109/TKDE.2005.99
Adomavicius, G., Huang, Z., & Tuzhilin, A. (2008). Personalization and recommender systems. In state-of-the-art decision-making tools in the information-intensive age (pp. 55-107). INFORMS. https://doi.org/10.1287/educ.1080.0044
Adomavicius, G., Sankaranarayanan, R., Sen, S., & Tuzhilin, A. (2005). Incorporating contextual information in recommender systems using a multidimensional approach. ACM transactions on information systems (TOIS)23(1), 103-145. https://doi.org/10.1145/1055709.1055714
Adomavicius, G., Tuzhilin, A., & Zheng, R. (2011). REQUEST: a query language for customizing recommendations. Information systems research22(1), 99-117.
Antoniou, G., & Van Harmelen, F. (2004). Web ontology language: Owl. In Handbook on ontologies (pp. 67-92). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24750-0_4
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: an R-tool for comprehensive science mapping analysis. Journal of informetrics11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Augstein, M., & Neumayr, T. (2019). 3. Automated personalization of input methods and processes. In Personalized human-computer interaction (pp. 67-102). De Gruyter Oldenbourg.
Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual review of information science and technology37(1), 179-255. https://doi.org/10.1002/aris.1440370106
Briner, R. B., & Denyer, D. (2012). Systematic review and evidence synthesis as a practice and scholarship tool. Handbook of evidence-based management: companies, classrooms and research, 112-129.
Broadus, R. N. (1987). Toward a definition of “bibliometrics”. Scientometrics12(5-6), 373-379. https://doi.org/10.1007/BF02016680
Burger, J. M. (2010). Personality. Cengage Learning.
Cantador, I., Fernández-Tobías, I., & Bellogín, A. (2013). Relating personality types with user preferences in multiple entertainment domains. CEUR workshop proceedings. Shlomo Berkovsky. http://hdl.handle.net/10486/665398
Chausson, O. (2010). Assessing the impact of gender and personality on film preferences.  Retrieved from https://www.semanticscholar.org/paper/Assessing-The-Impact-Of-Gender-And-Personality-On-Ersonality/682f92a3deedbed883b7fb7faac0f4f29fa46877 
Chen, Y., Wu, C., Xie, M., & Guo, X. (2011). Solving the sparsity problem in recommender systems using association retrieval. Journal of computers, 6(9), 1896-1902. DOI:10.4304/jcp.6.9.1896-1902
Choudhary, V., Ghose, A., Mukhopadhyay, T., & Rajan, U. (2005). Personalized pricing and quality differentiation. Management science51(7), 1120-1130. https://doi.org/10.1287/mnsc.1050.0383
Cobo, M. J., López‐Herrera, A. G., Herrera‐Viedma, E., & Herrera, F. (2011). Science mapping software tools: review, analysis, and cooperative study among tools. Journal of the American society for information science and technology62(7), 1382-1402. https://doi.org/10.1002/asi.21525
Crane, D. (1972). Invisible colleges; diffusion of knowledge in scientific communities. University of Chicago Press.
De Campos, L. M., Fernández-Luna, J. M., Huete, J. F., & Rueda-Morales, M. A. (2010). Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian networks. International journal of approximate reasoning51(7), 785-799. https://doi.org/10.1016/j.ijar.2010.04.001
Fernández-Tobías, I., Braunhofer, M., Elahi, M., Ricci, F., & Cantador, I. (2016). Alleviating the new user problem in collaborative filtering by exploiting personality information. User modeling and user-adapted interaction26(2), 221-255. https://doi.org/10.1007/s11257-016-9172-z
Ghazanfar, M. A., & Prugel-Bennett, A. (2010, January). A scalable, accurate hybrid recommender system. 2010 third international conference on knowledge discovery and data mining (pp. 94-98). IEEE. DOI: 10.1109/WKDD.2010.117
Ghiyasi, R. (2012). Designing a proposing system for managing the sending of money (Master Thesis, Qom University). (In Persian). Retrived from https://tm.balagh.ir/payannameh/12986
Goldenberg, D., Kofman, K., Albert, J., Mizrachi, S., Horowitz, A., & Teinemaa, I. (2021, March). Personalization in practice: methods and applications. Proceedings of the 14th ACM international conference on web search and data mining (pp. 1123-1126). https://doi.org/10.1145/3437963.3441657
Gorgoglione, M., Palmisano, C., & Tuzhilin, A. (2006, December). Personalization in context: does context matter when building personalized customer models?. Sixth international conference on data mining (ICDM'06) (pp. 222-231). IEEE. DOI: 10.1109/ICDM.2006.125
Hagen, P., Manning, H., & Souza, R. (1999). Smart personalization, forrester research. Cambridge, MA.
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM transactions on information systems (TOIS)22(1), 5-53. https://doi.org/10.1145/963770.963772
Hu, R., & Pu, P. (2011, October). Enhancing collaborative filtering systems with personality information. Proceedings of the fifth ACM conference on recommender systems (pp. 197-204). Association for Computing Machinery, New York. https://doi.org/10.1145/2043932.2043969
Jain, S., Grover, A., Thakur, P. S., & Choudhary, S. K. (2015, May). Trends, problems and solutions of recommender system. International conference on computing, communication & automation (pp. 955-958). IEEE. DOI: 10.1109/CCAA.2015.7148534
Jeckmans, A. J., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R. L., & Tang, Q. (2013). Privacy in recommender systems. In Social media retrieval (pp. 263-281). Springer, London. https://doi.org/10.1007/978-1-4471-4555-4_12
John, O. P., & Srivastava, S. (1999). The big-five trait taxonomy: history, measurement, and theoretical perspectives (Vol. 2, pp. 102-138). Berkeley: University of California.
Karimi, M., Jannach, D., & Jugovac, M. (2018). News recommender systems–survey and roads ahead. Information processing & management54(6), 1203-1227. https://doi.org/10.1016/j.ipm.2018.04.008
Kimball, R. (1996). The data warehouse toolkit: practical techniques for building dimensional data warehouses. John Wiley & Sons, Inc.
Lampropoulos, A. S., & Tsihrintzis, G. A. (2015). Machine learning paradigms, applications in recommender systems. Springer Cham.
Lika, B., Kolomvatsos, K., & Hadjiefthymiades, S. (2014). Facing the cold start problem in recommender systems. Expert systems with applications41(4), 2065-2073. https://doi.org/10.1016/j.eswa.2013.09.005
Liu, B., & Tuzhilin, A. (2008). Managing large collections of data mining models. Communications of the ACM51(2), 85-89. https://doi.org/10.1145/1314215.1314230
Murthi, B. P. S., & Sarkar, S. (2003). The role of the management sciences in research on personalization. Management science49(10), 1344-1362. https://doi.org/10.1287/mnsc.49.10.1344.17313
Nunes, M. A. S., & Hu, R. (2012, September). Personality-based recommender systems: an overview. Proceedings of the sixth ACM conference on recommender systems (pp. 5-6). Association for Computing Machinery, New York. https://doi.org/10.1145/2365952.2365957
Patel, B., Desai, P., & Panchal, U. (2017, March). Methods of recommender system: A review. 2017 international conference on innovations in information, embedded and communication systems (ICIIECS) (pp. 1-4). IEEE. DOI: 10.1109/ICIIECS.2017.8275856
Peppers, D., & Rogers, M. (1993). The one to one future: building relationships one customer at a time. New York: Currency Doubleday.
Pereira, N., & Varma, S. L. (2019). Financial planning recommendation system using content-based collaborative and demographic filtering. In Smart innovations in communication and computational sciences (pp. 141-151). Springer, Singapore. https://doi.org/10.1007/978-981-10-8968-8_12
Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of documentation25(4), 348-349.
Qiu, F., & Cho, J. (2006, May). Automatic identification of user interest for personalized search. Proceedings of the 15th international conference on world wide web (pp. 727-736). Association for Computing Machinery, New York. https://doi.org/10.1145/1135777.1135883
Ramakrishnan, N., Keller, B. J., Mirza, B. J., Grama, A. Y., & Karypis, G. (2001). When being weak is brave: privacy in recommender systems. Available at https://arxiv.org/abs/cs/0105028
Rashid, A. M., Albert, I., Cosley, D., Lam, S. K., McNee, S. M., Konstan, J. A., & Riedl, J. (2002, January). Getting to know you: learning new user preferences in recommender systems. Proceedings of the 7th international conference on intelligent user interfaces (pp. 127-134). Association for Computing Machinery, New York. https://doi.org/10.1145/502716.502737
Rentfrow, P. J., & Gosling, S. D. (2003). The do re mi's of everyday life: the structure and personality correlates of music preferences. Journal of personality and social psychology84(6), 1236-1256. https://doi.org/10.1037/0022-3514.84.6.1236
Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1-35). Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_1
Riecken, D. (2000). Introduction: personalized views of personalization. Communications of the ACM43(8), 26-28. https://doi.org/10.1145/345124.345133
Rousseau, D. M. (Ed.). (2012). The oxford handbook of evidence-based management. Oxford University Press.
Sarwar, B. M. (2001). Sparsity, scalability, and distribution in recommender systems. University of Minnesota.
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Application of dimensionality reduction in recommender system-a case study. Minnesota univ Minneapolis dept of computer science. DOI: 10.21236/ada439541
Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. Data mining and knowledge discovery5(1), 115-153. https://doi.org/10.1023/A:1009804230409
Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002, August). Methods and metrics for cold-start recommendations. Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval (pp. 253-260). Association for Computing Machinery, New York. https://doi.org/10.1145/564376.564421
Sheth, A., Bertram, C., Avant, D., Hammond, B., Kochut, K., & Warke, Y. (2002). Semantic content management for enterprises and the web. IEEE internet computing6(4), 80-87.
Surprenant, C. F., & Solomon, M. R. (1987). Predictability and personalization in the service encounter. Journal of marketing51(2), 86-96. https://doi.org/10.2307/1251131
Taqwa, M. R. & Abdollahi, H. (2014). Investigating the effect of five major personality factors on emotional intelligence and organizational improvement, management studies (improvement). Management studies (improvement and transformation), 22(72), 48-23. (In Persian). https://jmsd.atu.ac.ir/article_208.html
Tatiana, K., & Mikhail, M. (2018). Market basket analysis of heterogeneous data sources for recommendation system improvement. Procedia computer science, 136, 246–254. https://doi.org/10.1016/j.procs.2018.08.263
Thendral, S. E., & Valliyammai, C. (2018). Understanding personalization of recommender system: a domain perspective. International journal of applied engineering research13(15), 12422-12428.
Vesanen, J., & Raulas, M. (2006). Building bridges for personalization: a process model for marketing. Journal of interactive marketing20(1), 5-20. https://doi.org/10.1002/dir.20052
Zarei Matin, H. (2009). Advanced organizational behavior management. Agah Publication. (In Persian).  https://www.gisoom.com/book/1630170
Zibriczky12, D. (2016). Recommender systems meet finance: a literature review. Proceedings of the 2nd International Workshop on Personalization and Recommender Systems in Financial Services. Conference conducted at the meeting of CEUR Workshop Proceedings, Bari, Italy.
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational research methods18(3), 429-472. DOI: 10.1177/1094428114562629