Modern Portfolio Theory and its Applications in Information Retrieval

Rahmani, Mehdi

doi:10.22034/jipm.2024.710249

Modern Portfolio Theory and its Applications in Information Retrieval

Document Type : Original Article

Author

Mehdi Rahmani

Assistant Professor, Department of knowledge and information science, Isfahan University, Isfahan, Iran

10.22034/jipm.2024.710249

Abstract

Introduction and purpose: The portfolio theory is one of the theories in the financial field that was presented by Harry Markowitz. This theory states that investors should diversify their stock portfolio to reduce investment risk. This research has been conducted with the aim of investigating the applications of portfolio theory in information and knowledge retrieval. Method: The current research is applied in terms of purpose and qualitative research in terms of method. In the current study, the main concepts of modern portfolio theory were extracted using the method of content analysis and text analysis. In the next step, based on the method of evaluation and comparison, the functions of modern portfolio theory in information retrieval and the field of information science and epistemology were identified and determined. Finally, 12 experts in knowledge management, information and knowledge retrieval, indexing, cataloging, information systems, and librarians were selected as an expert panel. Then, during 7 sessions and by conducting interviews, using questionnaires, and holding individual and group meetings, the functions of modern portfolio theory were extracted. Findings: The results of the research showed that the modern theory of portfolio is effective in the process of information retrieval and can have an impact on 5 groups of creators, indexers, information system users, information systems,s and librarians and have a positive effect on improving the retrieval process. Conclusion: The results of the research showed that the portfolio theory has an impact on the influential groups in the recovery process and can play a role in improving the performance of the information recovery system. One of the most critical applications of portfolio theory is its use in ranking retrieved documents, increasing the connection between retrieved documents, reducing false fallout, increasing the percentage of information recovery, creating the possibility of semantic search, increasing comprehensiveness, and categorizing information based on subject areas. The current research can be the beginning of investigating other applications of the portfolio theory in information retrieval and makes it possible to carry out more detailed investigations in this field.

Keywords

Portfolio Theory

Information Retrieval

Badreh Theory

Subjects

Human and information

ابراهیم‌زاده، صنم، و سعید رضایی شریف‌آبادی. 1395. آموزه‌های نظریه اجتماعی-شناختی در بازیابی اطلاعات. تعامل انسان و اطلاعات، 3 (2): 29-42.

ارسطوپور، شعله، و اسدالله آزاد. 1386. نظریه برچسب‌گذاری و برچسب‌های موضوعی در سازماندهی اطلاعات: نگاهی تطبیقی از زاویه ارتباط‌های متقاعدگرانه. فصلنامه کتابداری و اطلاع‌رسانی 10( 4): 65-88.

امراللهی، محمود .1387. کارایی بدره اوراق بهادار شرکت‌های بیمه (تحلیل تطبیقی). صنعت بیمه 16 (2 پیاپی 54): 18-33.

بهزادی، حسن .1391. تأملی بر نقش باورهای معرفت‌شناختی در بازیابی اطلاعات و رفتار اطلاع‌یابی. فصلنامه نظام‌ها و خدمات اطلاعاتی 1 (2): 59-70.

جعفری، مهناز، نجلا حریری، و فهیمه باب‌الحوائجی. 1397. همبستگی نظریه امیدواری در دقت بازیابی اطلاعات از پایگاه‌های اطلاعاتی علوم پزشکی. پژوهشنامه پردازش و مدیریت اطلاعات 33 (3): 1065-1086.

خالویی، مرضیه.1387. ربط و مفهوم آن در بازیابی اطلاعات. علوم و فناوری اطلاعات 23 (3): 105-118.

رحمانی، مهدی، و محسن حاجی‌زین‌العابدینی. 1393-1394. مقایسه جامعیت و عملکرد پایگاه‌های اطلاعاتی ساینس دایرکت و اشپرینگر. فصلنامه نظام‌ها و خدمات اطلاعاتی 4 (1 و 2): 63-74.

فتاحی، رحمت‌الله. 1385. شناسایی و تحلیل واژگان عمومی در منابع وب: رویکردی نو به بسط عبارت جست‌وجو با استفاده از زبان طبیعی در موتورهای کاوش. مطالعات تربیتی و روان‌شناسی دانشگاه فردوسی مشهد، 25: 52-31.

فرهنگستان زبان و ادب فارسی. 1396. ذیل واژه بدره. بازیابی شده از http://www.persianacademy.ir/fa/word (دسترسی در 11/11/1396)

کریمی، المیرا، بابایی، محمود، و حسینی بهشتی، ملوک السادات. 1398. بررسی ویژگی‌های معنایی و هستی‌شناسانه نظام‌های بازیابی اطلاعات مبتنی‌بر اصطلاحنامه و هستی‌شناسی. پژوهشنامه پردازش و مدیریت اطلاعات 34 (4): 1585-1612.

متین‌فرد، مهران. 1382. مدلی برای بررسی کارایی بدره (پرتفوی) اوراق بهادار در شرکت‌های بیمه. فصلنامه صنعت بیمه 18 (3): 93-115.

نوروزی، علیرضا، و خالید ولایتی. 1389. نمایه‌سازی موضوعی: نمایه‌سازی مفهومی. تهران: انتشارات چاپار.

References:

Banawan, K., & S. Ulukus .2019. Noisy Private Information Retrieval: On Separability of Channel Coding and Information Retrieval. IEEE Transactions on Information Theory 65 (112): 8232-8249.

Câmara, A., D. Maxwell, & C. Hauff. 2022. Searching, learning, and subtopic ordering: A simulation-based analysis. In European Conference on Information Retrieval (pp. 142-156). Cham: Springer International Publishing.‏

Carmichael, B., G. B. Koumou, & K. Moran. 2015. Unifying Portfolio Diversification Measures Using Rao's Quadratic Entropy.CIRANO (Center for Interuniversity Research and Analysis of Organizations) Working Papers.

Chen, H., Y. Yang, W. Lu, & J. Chen. 2020. Exploring multiple diversification strategies for academic citation contexts recommendation. The Electronic Library 38 (4): 821-842.‏

Dudeja, R., J. Ma, & A. Maleki. 2020. Information Theoretic Limits for Phase Retrieval With Subsampled Haar Sensing Matrices. IEEE Transactions on Information Theory 66 (12): 8002-8045.

Erbacher, P., L. Denoyer, & L. Soulier. 2022. Interactive query clarification and refinement via user simulation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2420-2425).‏ Madrid, Spain.

Gordon, M. D., & P. Lenk. 1991. A utility theoretic examination of the probability ranking principle in information retrieval. JASIS 42 (10): 703–714.

Jones, C. 2017. Modern Portfolio Theory Digital Portfolio Theory and Intertemporal Portfolio Choice. PortfolioNetworks. American Journal of Industrial and Business Management 7: 833-854.

MacAvaney, S., C. Macdonald, R. Murray-Smith, & I. Ounis. 2021. IntenT5: Search Result Diversification using Causal Language Models. arXiv preprint arXiv:2108.04026.‏

Mothe, J., & M. Z. Ullah. 2023. Selective Query Processing: a Risk-Sensitive Selection of System Configurations. arXiv preprint arXiv:2305.18311.‏

Jafari Naini, K., I. Sengor Altingovde & W. Siberski. 2016. Scalable and efficient web search result diversification. ACM Trans. Web 10, 3, Article 15 (August 2016), 30 pages. DOI: http://dx.doi.org/10.1145/2907948

Pandari, A. R., & A. Azar, A. R. & Shavazi. 2012. Genetic algorithms for portfolio selection problem with non-linear objectives. African Journal of Business Management 6: 6209-6216.

Širůček, M. & K. Lukáš. 2015. Application of Markowitz Portfolio Theory by Building Optimal Portfolio on the US Stock Market. Published in: Acta Universitatis Agriculturae ET Silviculturae Mendelianae Brunensis 63 (4): 1375-1386.

Stirling, A. 2007. A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface 4 (15): 707–719.

Styltsvig, H. B. 2006. Ontology-based information retrieval. Denmark: PhD thesis, computer science section. Roskilde University.

Tian, T. 2020. On the Storage Cost of Private Information Retrieval, IEEE Transactions on Information Theory 66 (12): 7539-7549.

Tsai, M. F., & Ch. J. Wang. 2012. Post-Modern Portfolio Theory for Information Retrieval. Procedia Computer Science 13: 80-85.

Vithana, S., Banawan, K., & Ulukus, S. 2022. Semantic Private Information Retrieval, IEEE TRANSACTIONS ON INFORMATION THEORY, 68(4): 2635-2652.

Wang, J. & J. Zhu. 2009. Portfolio Theory of Information Retrieval. Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval Pages 115-122. https://dl.acm.org/citation.cfm?id=1571963 , Boston, MA, USA.

Wang, Zh., & S. Ulukus. 2022. Symmetric Private Information Retrieval at the Private Information Retrieval Rate, IEEE Journal on Selected Areas in Information Theory 3 (2): 350-361.

Wu, S., C. Huang, L. Li, & F. Crestani. 2019. Fusion-based methods for result diversification in web search. Information Fusion 45: 16-26.‏

Wu, S., Z. Zhang, & C. Xu. 2019. Evaluating the effectiveness of Web search engines on results diversification. Information Research: An International Electronic Journal 24 (1): n1.‏

Yu, H. T., A. Jatowt, R. Blanco, H. Joho, J. M. Jose, L. Chen, & F. Yuan. 2018. Revisiting the cluster-based paradigm for implicit search result diversification. Information Processing & Management 54 (4): 507-528.‏

Zamani, H., & M. Bendersky. 2023. Multivariate Representation Learning for Information Retrieval. arXiv preprint arXiv:2304.14522.‏

Zhang, R., Q. Ai, Y. Wu, Y. Ma, & Y. Liu. 2023. Diverse legal case search. arXiv preprint arXiv:2301.12504.‏